System and method for wireless location

ABSTRACT

Systems and methods for wireless location are disclosed. In one aspect, a method for wireless location includes collecting signal strength) values from one or more nodes (e.g., mobile devices) in an area over a time interval. The nodes receive wireless signals from one or more other transmitting nodes, where the signal strength values are representative of the signal strengths of the wireless signals. The method further includes normalizing the collected signal strength values and evaluating respective locations within the area of the nodes based on the normalized signal strength values. In a further aspect, the evaluated locations of the nodes may be used to execute an automated light show over the area, by instructing the nodes to display certain color or pattern at their locations in the area.

CROSS REFERENCE TO RELATED APPLICATION

This application claims the benefit of U.S. Provisional Application No.62/371,472, filed Aug. 5, 2016, the content of which is fullyincorporated by reference herein.

BACKGROUND OF THE INVENTION Field of the Invention

The present invention generally relates to system and method forlocating people or objects, and in particular, to a system and methodfor locating a wireless mobile station.

Discussion of the Background

There are deficiencies in systems and methods for wireless location inthe related art. For example, wireless location systems in the relatedart typically cannot provide a relative position within sub-meteraccuracy, without independent or specialized hardware. Also, it isdifficult for even certain equipments that include specialized wirelesslocation systems (e.g., Global Positioning System (GPS)) to achievesub-meter accuracy. Therefore, it is difficult for wireless locationsystems to determine the relative positions of wireless devices in arelatively small and crowded area (e.g., in an area where devices aresub-meter spaced apart).

SUMMARY OF THE INVENTION

Accordingly, the invention is directed to a system and method forwireless location that substantially obviates one or more of theproblems due to limitations and disadvantages of the related art.

An advantage of the invention is to provide relative position withinsub-meter of accuracy independent of hardware other than wireless mobilestation.

Another advantage of the invention is the ability to order the sequenceof devices as they appear near a targeted device e.g. the ability toknow what person is first in a line, know with certainty who is directlyadjacent to a selected device, deliver goods or services directly to thecorrect device, discern targeted devices in a crowd, etc.

Yet another advantage of the invention is to provide a dynamicallysequenced grid of wireless mobile stations at any location, independentof a network, to be used for any function in which location may benecessary but impossible using other methods, e.g., Global PositioningSystem (GPS), magnetic tracking, AP trilateration, etc.

Other systems in the related art pertaining to localization independentof GPS using received signal strength fail to provide a usableapproximation of location. Even after employing access points, knownRFID measurements, and advanced trilateration algorithms, no system hasbeen successfully deployed to localize a plurality of devices in realworld scenarios. Furthermore, the methods for testing the accuracy ofthe techniques of localization in the related art are derived from themethods used to measure the accuracy of GPS, e.g., accuracy of distanceapproximation between two devices and correlation with a referenceframe, such as the GPS coordinate plane (maintained by the InternationalCelestial Reference Frame). This mode of understanding accuracy isinsufficient and outdated when the use case of a crowd of devices is notbest served by a knowledge of the distance between two devices, but morepreferably by the order in which these devices appear relative to eachother.

Embodiments of the present invention achieve sub-meter radial accuracy,as understood by relative distance, relative position to other devices,and if available, a localized reference frame.

The present disclosure can provide a number of advantages depending onthe particular aspect, embodiment, and/or configuration. These and otheradvantages will be apparent from the disclosure.

According to an embodiment, a method for wireless location comprisescollecting a plurality of signal strength values from a plurality ofnodes over a time interval, wherein the signal strength values arerepresentative of signal strengths of respective plurality of wirelesssignals transmitted by at least one of the nodes to at least one otherof the nodes, and wherein the nodes are located in an area; normalizingthe collected signal strength values; and evaluating respectivelocations within the area of the nodes based on the normalized signalstrength values. The method further comprises orienting the locationcoordinates to an orientation representative of an orientation of thenodes. The area includes an indoor facility. The area is less than 40 mby 40 m. A distance between at least two of the nodes is less than anaccuracy of Global Positioning System (GPS). The nodes comprise mobiledevices. At least one of the nodes include a different equipment fortransmitting or receiving the wireless signal than at least one other ofthe nodes. The signal strength values comprise received signal strengthindicator (RSSI) values. The wireless signals comprise Wi-Fi signals.The wireless signals comprise Bluetooth signals. The wireless signalscomprise wireless signals of communication in a mesh network, andwherein at least two of the nodes are part of the mesh network. Thenormalizing comprises normalizing the signal strength values with anormalization function, wherein the normalization function is based on afit of previously collected signal strength values to a pre-determinednormalization range. The previously collected signal strength values arecollected from nodes with similar groups of wireless equipments as thenodes for transmitting or receiving the wireless signals. The fitcomprises an exponential fit. The normalization function is based on afit of averages of the previously collected signal strength values ofnodes at a substantially same distance. The normalization function isbased on a fit of averages of the previously collected signal strengthvalues of nodes at a substantially same distance. The evaluatingcomprises evaluating using machine learning technique on the normalizedsignal strength values. The machine learning technique comprises aself-organizing map (SOM). Parameters for the SOM comprise a learningrate between 300 to 1000. Parameters for the SOM comprise a sigma ratebetween 1.0 to 20.0. The machine learning technique is trained tominimize an error to the locations. The locations comprise distancesbetween the nodes. The locations comprise directions between the nodes.The evaluating comprises evaluating using a force directed graph. Theorienting comprises evaluating the orientation using singular valuedecomposition. The method further comprises transmitting respective datato at least one of the nodes for controlling respective displays of thenodes based on the data. The transmitting is synchronized with musicplaying in the area in substantially real-time. The evaluating isperformed by a server. The evaluating is performed by the mesh network.

In another embodiment, a wireless location system, comprises a pluralityof wireless nodes positioned in an area, wherein at least one of thenodes is transmitting one or more wireless signals in the area, whereinthe wireless signals are received by at least one of the other nodes,wherein signal strengths of the respective wireless signals are detectedby the at least one other nodes as respective signal strength values;and one or more computational equipments configured for normalizing thecollected signal strength values and evaluating respective locationswithin the area of the nodes based on the normalized signal strengthvalues. The computational equipments are further configured fororienting the location coordinates to an orientation representative ofan orientation of the nodes. The area includes an indoor facility. Thearea is less than 40 m by 40 m. A distance between at least two of thenodes is less than an accuracy of Global Positioning System (GPS). Adistance between at least two of the nodes is less than 5 m. The nodescomprise mobile devices. At least one of the nodes include a differentequipment for transmitting or receiving the wireless signal than atleast one other of the nodes. The signal strength values comprisereceived signal strength indicator (RSSI) values. The wireless signalscomprise Wi-Fi signals. The wireless signals comprise Bluetooth signals.The wireless signals comprise wireless signals of communication in amesh network, and wherein at least two of the nodes are part of the meshnetwork. The normalizing comprises normalizing the signal strengthvalues with a normalization function, wherein the normalization functionis based on a fit of previously collected signal strength values to apre-determined normalization range. The previously collected signalstrength values are collected from nodes with similar groups of wirelessequipments as the nodes for transmitting or receiving the wirelesssignals. The fit comprises an exponential fit. The normalizationfunction is based on a fit of averages of the previously collectedsignal strength values of nodes at a substantially same distance. Thenormalization function is based on a fit of averages of the previouslycollected signal strength values of nodes at a substantially samedistance. The evaluating comprises evaluating using machine learningtechnique on the normalized signal strength values. The machine learningtechnique comprises a self-organizing map (SOM). Parameters for the SOMcomprise a learning rate between 300 to 1000. Parameters for the SOMcomprise a sigma rate between 1.0 to 20.0. The machine learningtechnique is trained to minimize an error to the locations. Thelocations comprise distances between the nodes. The locations comprisedirections between the nodes. The evaluating comprises evaluating usinga force directed graph. The orienting comprises evaluating theorientation using singular value decomposition. The computationalequipments are further configured for transmitting respective data to atleast one of the nodes for controlling respective displays of the nodesbased on the data. The transmitting is synchronized with music playingin the area in substantially real-time. The computational equipmentscomprise a server. The computational equipments comprise the nodes thatare part of the mesh network.

In a further embodiment, a method for wireless location, comprisescollecting a plurality of signal strength values from a plurality ofnodes over a time interval, wherein the signal strength values arerepresentative of signal strengths of respective plurality of wirelesssignals transmitted by at least one of the nodes to at least one otherof the nodes, and wherein the nodes are located in an area; normalizingthe collected signal strength values, wherein the normalizing comprisesnormalizing the signal strength values with a normalization function,wherein the normalization function is based on a fit of previouslycollected signal strength values to a pre-determined normalizationrange; evaluating respective locations within the area of the nodesbased on the normalized signal strength values, wherein the evaluatingcomprises evaluating using machine learning technique on the normalizedsignal strength values, and wherein the machine learning techniquecomprises a self-organizing map (SOM); and orienting the locationcoordinates to an orientation representative of an orientation of thenodes, wherein the area includes an indoor facility less than 40 m by 40m, wherein a distance between at least two of the nodes is less than 5m, wherein the nodes comprise mobile devices. At least one of the nodesinclude a different equipment for transmitting or receiving the wirelesssignal than at least one other of the nodes. The signal strength valuescomprise received signal strength indicator (RSSI) values. The wirelesssignals comprise Wi-Fi signals. The wireless signals comprise Bluetoothsignals. The wireless signals comprise wireless signals of communicationin a mesh network, and wherein at least two of the nodes are part of themesh network. The previously collected signal strength values arecollected from nodes with similar groups of wireless equipments as thenodes for transmitting or receiving the wireless signals. The fitcomprises an exponential fit. The normalization function is based on afit of averages of the previously collected signal strength values ofnodes at a substantially same distance. The normalization function isbased on a fit of averages of the previously collected signal strengthvalues of nodes at a substantially same distance. Parameters for the SOMcomprise a learning rate between 300 to 1000. Parameters for the SOMcomprise a sigma rate between 1.0 to 20.0. The machine learningtechnique is trained to minimize an error to the locations. Thelocations comprise distances between the nodes. The locations comprisedirections between the nodes. The evaluating comprises evaluating usinga force directed graph. The orienting comprises evaluating theorientation using singular value decomposition. The method furthercomprises transmitting respective data to at least one of the nodes forcontrolling respective displays of the nodes based on the data. Thetransmitting is synchronized with music playing in the area insubstantially real-time. The evaluating is performed by a server. Theevaluating is performed by the mesh network.

The terms described below are provided for convenience in understandingat least one embodiment of the present disclosure. Thus, the termdescriptions following do not serve to necessarily define or limit thescope of these terms in all embodiments disclosed herein. In general,the term descriptions immediately below are also referenced in variousportions of this disclosure of which such portions may expand upon theseterms.

As used herein, the term crowd may, inter alia, refer to a plurality ofdevices in a locality. It is understood that an attempt to localizedevices independent of GPS may require a plurality of devices and that ascenario in which such localization is desired may have a plurality ofsuch devices available (e.g., in households, buildings, malls, airports,urban streets, etc.). Accordingly, embodiments of the present inventioninclude novel methods for understanding accuracy of device-to-devicelocalization, which may comprise one or more of:

-   -   a. the ability to find another device in the crowd with an        acceptable percentage of success;    -   b. the ability to determine the sequence, or order, of devices        within the crowd with an acceptable percentage of success; and    -   c. an ability to locate a device within a crowd to an acceptable        degree of accuracy with relativity to the other devices.

As used herein, wireless mobile station or mobile device may refer to awireless device that is at least a transmitting device, and in mostcases is also a wireless receiving device, such as a portable radiotelephony handset, Bluetooth beacon, mobile device, tablet, personalcomputer, automated teller, commercial register, etc.

The phrases “at least one,” “one or more,” and “and/or” are open-endedexpressions that are both conjunctive and disjunctive in operation. Forexample, each of the expressions “at least one of A, B and C,” “at leastone of A, B, or C,” “one or more of A, B, and C,” “one or more of A, B,or C” and “A, B, and/or C” means A alone, B alone, C alone, A and Btogether, A and C together, B and C together, or A, B and C together.

The term “a” or “an” entity refers to one or more of that entity. Assuch, the terms “a” (or “an”), “one or more” and “at least one” can beused interchangeably herein. It is also to be noted that the terms“comprising,” “including,” and “having” can be used interchangeably.

The term “automatic” and variations thereof, as used herein, refers toany process or operation done without material human input when theprocess or operation is performed. However, a process or operation canbe automatic, even though performance of the process or operation usesmaterial or immaterial human input, if the input is received beforeperformance of the process or operation. Human input is deemed to bematerial if such input influences how the process or operation will beperformed. Human input that consents to the performance of the processor operation is not deemed to be “material.”

The term “module,” as used herein, refers to any known or laterdeveloped hardware, software, firmware, artificial intelligence, fuzzylogic, or combination of hardware and software that is capable ofperforming the functionality associated with that element.

The terms “determine,” “calculate,” and “compute,” and variationsthereof, as used herein, are used interchangeably and include any typeof methodology, process, mathematical operation or technique.

It shall be understood that the term “means,” as used herein, shall begiven its broadest possible interpretation in accordance with 35 U.S.C.,Section 112(f). Accordingly, a claim incorporating the term “means”shall cover all structures, materials, or acts set forth herein, and allof the equivalents thereof. Further, the structures, materials or actsand the equivalents thereof shall include all those described in thesummary of the invention, brief description of the drawings, detaileddescription, abstract, and claims themselves.

The preceding is a simplified summary of the disclosure to provide anunderstanding of some aspects of the disclosure. This summary is neitheran extensive nor exhaustive overview of the disclosure and its variousaspects, embodiments, and/or configurations. It is intended neither toidentify key or critical elements of the disclosure nor to delineate thescope of the disclosure but to present selected concepts of thedisclosure in a simplified form as an introduction to the more detaileddescription presented below. As will be appreciated, other aspects,embodiments, and/or configurations of the disclosure are possible,utilizing, alone or in combination, one or more of the features setforth above or described in detail below.

Additional features and advantages of the invention will be set forth inthe description which follows, and in part will be apparent from thedescription, or may be learned by practice of the invention. Theobjectives and other advantages of the invention will be realized andattained by the structure particularly pointed out in the writtendescription and claims hereof as well as the appended drawings.

This summary section is neither intended to be, nor should be, construedas being representative of the full extent and scope of the presentdisclosure. Additional benefits, features and embodiments of the presentdisclosure are set forth in the attached figures and in the descriptionherein below, and as described by the claims. Accordingly, it should beunderstood that this Summary section may not contain all of the aspectsand embodiments claimed herein.

Additionally, the disclosure herein is not meant to be limiting orrestrictive in any manner. Moreover, the present disclosure is intendedto provide an understanding to those of ordinary skill in the art of oneor more representative embodiments supporting the claims. Thus, it isimportant that the claims be regarded as having a scope includingconstructions of various features of the present disclosure insofar asthey do not depart from the scope of the methods and apparatusesconsistent with the present disclosure (including the originally filedclaims). Moreover, the present disclosure is intended to encompass andinclude obvious improvements and modifications of the presentdisclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a diagram of an exemplary communication systembetween two mobile stations.

FIG. 2 illustrates a flow diagram of an exemplary method of wirelesslocation according to an embodiment.

FIG. 3 shows a photograph of an exemplary experimental environment setupfor a crowd of mobile devices in sub-meter spacing according to anembodiment.

FIG. 4 illustrates an exemplary layout diagram for the phone layout ofexperimental test 1.

FIG. 5 illustrates an exemplary layout diagram for the phone layout ofexperimental test 2.

FIG. 6 illustrates an exemplary layout diagram for the phone layout ofexperimental tests 3-5 and tests 6-20;

FIG. 7 illustrates an exemplary diagram for Determining Phone to PhoneRSSI Connection Strength with Kalman Filter in Test 19 according to anembodiment;

FIG. 8 illustrates an exemplary diagram for Kalman Filtered ForceDirected Graph Drawing Using Force Directed Graph Drawing Library D3jsin Test 19;

FIG. 9 illustrates an exemplary diagram for determining phone to phoneconnection strength with max filter in Test 19 according to anembodiment;

FIG. 10 illustrates an exemplary diagram of max filtered force directedgraph drawing using force directed graph drawing library D3.js for Test19 according to an embodiment;

FIG. 11 illustrates a graph of max filtered RSSI scaled value accuracyfor Test 19. FIG. 6 is referenced for test layout nodes;

FIG. 12 illustrates a graph for comparison with GPS accuracy for Test20;

FIG. 13 illustrates an exemplary flow diagram of a wireless locationmethod according to an embodiment;

FIG. 14A illustrates an arrangement of nodes for Test A according to anembodiment;

FIG. 14B illustrates an arrangement of nodes for Test B according to anembodiment;

FIG. 14C illustrates an arrangement of nodes for Test C according to anembodiment;

FIG. 14D illustrates an arrangement of nodes for Test D according to anembodiment;

FIG. 15A illustrates a graph showing measured relationship between RSSIvalue and distance for Tests A-D with 5 sec RSSI reading interval;

FIG. 15B illustrates a graph showing measured relationship between RSSIvalue and distance with an exponential fit of the graph for Tests A-Dwith 5 sec RSSI reading interval;

FIG. 15C illustrates a graph showing measured relationship between RSSIvalue and distance with outliers removed for Tests A-D with 5 sec RSSIreading interval;

FIG. 15D illustrates a graph showing measured relationship between RSSIvalue and mean distances with outliers removed for Tests A-D with 5 secRSSI reading interval;

FIG. 15E illustrates a graph showing measured relationship between RSSIvalue and total mean distances with outliers removed and an exponentialfit of the graph for Tests A-D with 5 sec RSSI reading interval;

FIG. 16 illustrates an exemplary comparison between positions of nodesto SOM evaluated positions (without orientation) for Test B with 5 secRSSI reading interval.

FIG. 17A illustrates a comparison between positions of nodes to SOMevaluated positions after orientation for Test A with 5 sec RSSI readinginterval;

FIG. 17B illustrates a comparison between positions of nodes to SOMevaluated positions after orientation for Test B with 5 sec RSSI readinginterval;

FIG. 17C illustrates a comparison between positions of nodes to SOMevaluated positions after orientation for Test C with 5 sec RSSI readinginterval;

FIG. 17D illustrates a comparison between positions of nodes to SOMevaluated positions after orientation of Test D with 5 sec RSSI readinginterval;

FIG. 18A illustrates a comparison between positions of nodes to SOMevaluated positions after orientation for Test A with 30 sec RSSIreading interval;

FIG. 18B illustrates a comparison between positions of nodes to SOMevaluated positions after orientation for Test B with 30 sec RSSIreading interval;

FIG. 18C illustrates a comparison between positions of nodes to SOMevaluated positions after orientation for Test C with 30 sec RSSIreading interval;

FIG. 18D illustrates a comparison between positions of nodes to SOMevaluated positions after orientation for Test D with 30 sec RSSIreading interval;

FIG. 19A illustrates a comparison between positions of nodes to SOMevaluated positions after orientation for Test A with 1 min RSSI readinginterval;

FIG. 19B illustrates a comparison between positions of nodes to SOMevaluated positions after orientation for Test B with 1 min RSSI readinginterval;

FIG. 19C illustrates a comparison between positions of nodes to SOMevaluated positions after orientation for Test C with 1 min RSSI readinginterval;

FIG. 19D illustrates a comparison between positions of nodes to SOMevaluated positions after orientation for Test D with 1 min RSSI readinginterval;

FIG. 20A illustrates a comparison between positions of nodes to SOMevaluated positions after orientation for Test A with 2 min RSSI readinginterval;

FIG. 20B illustrates a comparison between positions of nodes to SOMevaluated positions after orientation for Test B with 2 min RSSI readinginterval;

FIG. 20C illustrates a comparison between positions of nodes to SOMevaluated positions after orientation for Test C with 2 min RSSI readinginterval;

FIG. 20D illustrates a comparison between positions of nodes to SOMevaluated positions after orientation for Test D with 2 min RSSI readinginterval;

FIG. 21A illustrates a comparison among the average error rates of SOMevaluated positions with different SOM parameters for 5 sec RSSI readinginterval;

FIG. 21B illustrates a comparison among the average error rates of SOMevaluated positions with different SOM parameters for 30 sec RSSIreading interval;

FIG. 21C illustrates a comparison among the average error rates of SOMevaluated positions with different SOM parameters for 1 min RSSI readinginterval; and

FIG. 21D illustrates a comparison among the average error rates of SOMevaluated positions with different SOM parameters for 2 min RSSI readinginterval.

In the appended figures, similar components and/or features may have thesame reference label. Further, various components of the same type maybe distinguished by following the reference label by a letter thatdistinguishes among the similar components. If only the first referencelabel is used in the specification, the description is applicable to anyone of the similar components having the same first reference labelirrespective of the second reference label.

DETAILED DESCRIPTION OF THE ILLUSTRATED EMBODIMENTS

In order to more fully appreciate the present disclosure and to provideadditional related features, the following references are incorporatedherein by reference in their entirety:

(1) U.S. Pat. No. 7,312,752 by Smith, et al., which discloses techniquesfor accurate position location and tracking suitable for a wide range offacilities in variable environments are disclosed. In one aspect, asystem for position location comprises a plurality of sensors (e.g. anetwork monitor, an environment sensor) for generating measurements of aplurality of sources, a plurality of objects or tags, each objectgenerating measurements of the plurality of sources, and a processor forreceiving the measurements and generating a position location for one ormore objects in accordance with the received measurements. In anotheraspect, a position engine comprises a mapped space of a physicalenvironment, and a processor for updating the mapped space in responseto received measurements. The position engine may receive secondmeasurements from an object within the physical environment, andgenerate a position location estimate for the object from the receivedsecond measurements and the mapped space. Various other aspects are alsopresented;

(2) U.S. Pat. No. 8,077,090 by Chintalapudi, et al., which disclosessimultaneous localization and RF modeling technique that pertains to amethod of providing simultaneous localization and radio frequency (RF)modeling. In one embodiment, the technique operates in a space withwireless local area network coverage (or other RF transmitters). Userscarrying Wi-Fi-enabled devices traverse this space while the mobiledevices record the Received Signal Strength (RSS) measurementscorresponding to access points (APs) in view at various unknownlocations and report these RSS measurements, as well as any otheravailable location fix to a localization server. A RF modeling algorithmruns on the server and is used to estimate the location of the APs usingthe recorded RSSI measurements and any other available locationinformation. All of the observations are constrained by the physics ofwireless propagation. The technique models these constraints and uses agenetic algorithm to solve them, thereby providing an absolute locationof the mobile device;

(3) U.S. Patent Application Publication No. 2008/0188242, by Carlson, etal., which discloses the location of a wireless mobile device may beestimated using, at least in part, one or more pre-existing NetworkMeasurement Reports (“NMRs”) which include calibration data for a numberof locations within a geographic region. The calibration data for theselocations is gathered and analyzed so that particular grid points withinthe geographic region can be determined and associated with a particularset or sets of calibration data from, for example, one or more NMRs.Embodiments of the present subject matter also provide a method ofimproving a location estimate of a mobile device. Received signal levelmeasurements reported by a mobile device for which a location estimateis to be determined may be evaluated and/or compared with thecharacteristics associated with the various grid points to estimate thelocation of the mobile device;

(4) U.S. Patent Application Publication No. 2010/0073235 by Smith, etal., which discloses techniques for accurate position location andtracking suitable for a wide range of facilities in variableenvironments are disclosed. In one aspect, a system for positionlocation comprises a plurality of sensors (e.g. a network monitor, anenvironment sensor) for generating measurements of a plurality ofsources, a plurality of objects or tags, each object generatingmeasurements of the plurality of sources, and a processor for receivingthe measurements and generating a position location for one or moreobjects in accordance with the received measurements. In another aspect,a position engine comprises a mapped space of a physical environment,and a processor for updating the mapped space in response to receivedmeasurements. The position engine may receive second measurements froman object within the physical environment, and generate a positionlocation estimate for the object from the received second measurementsand the mapped space; and

(5) Yang, et al., “Beyond Trilateration: On the Localizability ofWireless Ad-hoc Networks”, IEEE/ACME Transactions on Networking, Vol.18, No. 6 Dec. 2010, which discloses localization being an essentialservice for many wireless sensor network applications. While severallocalization schemes rely on anchor nodes and range measurements toachieve fine-grained positioning, it proposed a range-free, anchor-freesolution that works using connectivity information only. The approach,suitable for deployments with strict cost constraints, is based on theneural network paradigm of Self-Organizing Maps (SOM). A lightweightSOM-based algorithm to compute virtual coordinates that are effectivefor location-aided routing was presented. This algorithm can alsoexploit the location information, if available, of few anchor nodes tocompute absolute positions. Results of extensive simulations showimprovements over the popular Multi-Dimensional Scaling (MDS) scheme,especially for networks with low connectivity, which are intrinsicallyharder to localize, and in presence of irregular radio pattern oranisotropic deployment. It was analytically demonstrated that theproposed scheme has low computation and communication overheads; hence,making it suitable for resource-constrained networks.

Embodiments herein presented are not exhaustive, and further embodimentsmay be now known or later derived by one skilled in the art.

Functional units described in this specification and figures may belabeled as modules, or outputs in order to more particularly emphasizetheir structural features. A module and/or output may be implemented ashardware, e.g., comprising circuits, gate arrays, off-the-shelfsemiconductors such as logic chips, transistors, or other discretecomponents. They may be fabricated with Very-large-scale integration(VLSI) techniques. A module and/or output may also be implemented inprogrammable hardware such as field programmable gate arrays,programmable array logic, programmable logic devices or the like.Modules may also be implemented in software for execution by varioustypes of processors. In addition, the modules may be implemented as acombination of hardware and software in one embodiment.

An identified module of programmable or executable code may, forinstance, include one or more physical or logical blocks of computerinstructions that may, for instance, be organized as an object,procedure, or function. Components of a module need not necessarily bephysically located together but may include disparate instructionsstored in different locations which, when joined logically together,include the module and achieve the stated function for the module. Thedifferent locations may be performed on a network, device, server, andcombinations of one or more of the same. A module and/or a program ofexecutable code may be a single instruction, or many instructions, andmay even be distributed over several different code segments, amongdifferent programs, and across several memory devices. Similarly, dataor input for the execution of such modules may be identified andillustrated herein as being an encoding of the modules, or being withinmodules, and may be embodied in any suitable form and organized withinany suitable type of data structure.

In one embodiment, the system, components and/or modules discussedherein may include one or more of the following: a server or othercomputing system including a processor for processing digital data,memory coupled to the processor for storing digital data, an inputdigitizer coupled to the processor for inputting digital data, anapplication program stored in one or more machine data memories andaccessible by the processor for directing processing of digital data bythe processor, a display device coupled to the processor and memory fordisplaying information derived from digital data processed by theprocessor, and a plurality of databases or data management systems.

In one embodiment, functional block components, screen shots, userinteraction descriptions, optional selections, various processing steps,and the like are implemented with the system. It should be appreciatedthat such descriptions may be realized by any number of hardware and/orsoftware components configured to perform the functions described.Accordingly, to implement such descriptions, various integrated circuitcomponents, e.g., memory elements, processing elements, logic elements,look-up tables, input-output devices, displays and the like may be used,which may carry out a variety of functions under the control of one ormore microprocessors or other control devices.

In one embodiment, software elements may be implemented with anyprogramming, scripting language, and/or software developmentenvironment, e.g., Fortran, C, C++, C#, COBOL, Apache Tomcat, SpringRoo, Web Logic, Web Sphere, assembler, PERL, Visual Basic, SQL, SQLStored Procedures, AJAX, extensible markup language (XML), Arduino,Flex, Flash, Java, .Net and the like. Moreover, the variousfunctionalities in the embodiments may be implemented with anycombination of data structures, objects, processes, routines or otherprogramming elements.

In one embodiment, any number of conventional techniques for datatransmission, signaling, data processing, network control, and the likeas one skilled in the art will understand may be used. Further,detection or prevention of security issues using various techniquesknown in the art, e.g., encryption, may be also be used in embodimentsof the invention. Additionally, many of the functional units and/ormodules, e.g., shown in the figures, may be described as being “incommunication” with other functional units and/or modules. Being “incommunication” refers to any manner and/or way in which functional unitsand/or modules, such as, but not limited to, input/output devices,computers, laptop computers, PDAs, mobile devices, smart phones,modules, and other types of hardware and/or software may be incommunication with each other. Some non-limiting examples includecommunicating, sending and/or receiving data via a network, a wirelessnetwork, software, instructions, circuitry, phone lines, Internet lines,fiber optic lines, satellite signals, electric signals, electrical andmagnetic fields and/or pulses, and/or the like and combinations of thesame.

By way of example, communication among the users, subscribers and/orserver in accordance with embodiments of the invention may beaccomplished through any suitable communication channels, such as, forexample, a telephone network, an extranet, an intranet, the Internet,cloud based communication, point of interaction devices (point of saledevice, personal digital assistant, cellular phone, kiosk, and thelike), online communications, off-line communications, wirelesscommunications, RF communications, cellular communications, Wi-Ficommunications, transponder communications, local area network (LAN)communications, wide area network (WAN) communications, networked orlinked devices and/or the like. Moreover, although embodiments of theinvention may be implemented with TCP/IP communications protocols, othertechniques of communication may also be implemented using IEEEprotocols, IPX, Appletalk, IP-6, NetBIOS, OSI or any number of existingor future protocols. Specific information related to the protocols,standards, and application software utilized in connection with theInternet is generally known to those skilled in the art and, as such,need not be detailed herein.

In embodiments of the invention, the system provides and/or receives acommunication or notification via the communication system to or from anend user. The communication is typically sent over a network, e.g., acommunication network. The network may utilize one or more of aplurality of wireless communication standards, protocols or wirelessinterfaces (including LTE, CDMA, WCDMA, TDMA, UMTS, GSM, GPRS, OFDMA,WiMAX, FLO TV, Mobile DTV, WLAN, and Bluetooth technologies), and may beprovided across multiple wireless network service providers. The systemmay be used with any mobile communication device service (e.g., texting,voice calls, games, videos, Internet access, online books, etc.), SMS,MMS, email, mobile, land phone, tablet, smartphone, television,vibrotactile glove, voice carry over, video phone, pager, relay service,teletypewriter, and/or GPS and combinations of the same.

Reference will now be made in detail to embodiments of the presentinvention, an example of which is illustrated in the accompanyingdrawings.

FIG. 1 illustrates a diagram of an exemplary communication systembetween two mobile stations.

In communication system 100, mobile stations 101A and 101B each includesa transceiver for transmitting and receiving wireless signals in one ormore wireless distribution methods (e.g., Bluetooth, Wi-fi, etc).Therefore, when mobile stations 101A and 101B are each capable ofreceiving wireless signals from the other mobile station when in rangeand using the same wireless distribution method. The received wirelesssignals have received signal strength indicators (RSSI) as measurementsof the signal strength or power of the received wireless signals. TheRSSI depends on the wireless channel and/or environment, and isgenerally a function of the distance between mobile stations 101A and101B (e.g., the RSSI tends to decrease as the mobile stations 101A and101B are farther apart).

In one embodiment, received signal strength, e.g., RSSI from Bluetoothsignal can be used to obtain or estimate location. For example, locationof one or more mobile devices may be obtained or estimated bytriangulation and/or trilateration. In another embodiment, the receivedsignal strength may be utilize to obtain location and/or increase thecertainty of location of the mobile device, e.g., a received BluetoothRSSI may be a solution as there are multitudes of applications beyondour chosen lightshow/event management use case.

It is recognized that there exist some technical issues including poorlocation accuracy when using RSSI. However, the issues may be overcomeby a deployment of multiple mobile devices according to an embodiment.For example, Bluetooth networks used in rooms of crowds of people (e.g.,multiple mobile devices), as opposed to isolated rooms, can lead togreater amounts of data, thereby resulting in greater accuracy. In oneexample test herein below, twenty one mobile devices, e.g., MotorolaMoto E smart phones, were used to show increased accuracy.

In an embodiment, the relative positions (e.g., a position that relatesto the order, or the sequence of one or more devices from any referencepoint, providing a reasonable approximation of the direction of devicesrelative to one or more of other devices), and not merely the actualposition (e.g., a position (which may be estimated) that may berepresentative of the real location of the device in relation to theEarth, the surrounding environment and/or a reference location), of themobile devices may be obtained or estimated. In one embodiment, therelative position can be used to estimate or predict an order in whichthe mobile devices are organized relative to each other. In oneembodiment, the relative position can be used to estimate or predict theabsolute position (e.g., a position known or estimated by a distance anda direction of each device relative to one or more of another, withoutnecessarily having the knowledge of an orientation to the surroundingenvironment), which can then be oriented to predict or estimate anactual position.

One embodiment directed towards a coordinated light-show using mobiledevices as pixels in a crowd-sized screen may be accomplished. Forexample, once one or more of the actual position, the relative position,and/or the absolute position of at least a portion of the mobile devicescan be determined or estimated in a crowd, the system may control suchmobile devices to display a portion of a larger display (e.g., based onthe position of the respective mobile devices). Therefore, thecombination of the displays of the mobile devices at their respectivepositions may at least appear to make up the larger display when thecrowd is viewed. In an embodiment, the displays may be controlledrhythmically (e.g., following the beat of music). In an embodiment, thedisplays may be automatically controlled through a program (e.g.,programmed to display through a disc jockey (DJ) console through aMusical Instrument Digital Interface (MIDI)).

In an embodiment, no additional hardware (besides the mobile devices)may be needed (e.g., the mobile devices may be controlled through an appon the mobile devices and uses RSSI of the various received wirelesssignals supported by the mobile devices). This is advantageous for anumber of reasons including decreasing costs, decreasing complexities,and allowing for ease of implementation, e.g., remote implementation.One of skill in the art could add and/or use additional hardware as nowknown or may be later derived. Optionally and/or alternatively, datastreaming is not used to obtain location of the mobile devices. Inanother embodiment, the received signal strength may be utilize toobtain location and/or increase the certainty of location of the mobiledevice, e.g., a received Bluetooth RSSI may be a solution as there aremultitudes of applications beyond a chosen light/display show and/orevent management use case as previously discussed.

In one embodiment, embodiments of the sequencing and localizationdiscussed in this disclosure may also be used to direct, coordinate, andaid emergency personnel responding to injury, overdose, theft, securitythreat, or any other concern to the public safety. In one example,embodiments can be used to alert authorities the location and type ofemergency. In one example, the invention can be used to direct emergencyresponders to the locale of the emergency using phone screen cues, audiocommands, and other direction techniques as now known or may be laterderived. In one embodiment, embodiments can be used to direct civiliansaway from emergencies or toward safe areas. Embodiments are advantageousfor emergency routing because they have the capacity to be independentof both a data network and energy grid as well as not significantlyaffected by environment.

In one embodiment, instead of attempting to perfect RSSI distanceregressions, as is the method of GPS and related art, a device-to-deviceactual positioning can be used with relative position understood as anorder of appearance, or sequence to determine location of the mobiledevice in a crowd of devices. Understanding that applications wherelocalization is desirable but GPS is inadequate are naturally use casesinvolving a crowd, or a plurality of devices in proximity, such as inhouseholds, offices, airports, festivals, etc. In such use cases,precise location may be necessary to a tolerance yet achieved by GPS orBLE trilateration. In such use cases, distance between nodes is not asrelevant as the relative direction between nodes or the relativesequence of nodes. Relative positioning is a much more approachableproblem to solve. It can be used to achieve an awareness of theapproximate location of devices allowing for the deployment of mostwanted localization functions, e.g., finding a device, delivering to adevice, collecting movement data, counting number of devices in an area,communicating with select localized groups, etc. In one embodiment, therelative positioning of the mobile devices can be used to determineabsolution location at sub-meter resolution.

FIG. 2 illustrates a flow diagram of an exemplary method of wirelesslocation according to an embodiment.

Wireless location method 200 performs data collection, sorting, andusing a force directed graph to determine relative location of nodes(e.g., mobile devices). The method 200 starts with recording RSSI datathrough nodes 210. In an embodiment, each node may include transmittersand receivers (e.g., mobile devices capable of both transmitting andreceiving wireless signals from other mobile devices). Therefore, eachnode may receive and gather connection data (including RSSI data) withother nodes. In another embodiment, not all nodes may both transmit orreceive data (some may perform only one of transmitting or receiving).Therefore, the nodes may include incomplete connection and RSSI data.The incomplete data may be estimated, predicted, and/or extrapolatedusing techniques as known now or may be later derived.

The connection data received (including RSSI data) may be extracted andsorted 220. In an embodiment, the RSSI data may be extracted and/orsorted at each node or may be sorted at one or more centralized location(e.g., a centralized server or distributed servers). The extraction andsorting may include additional pre-processing such as normalization ofthe data based on the wireless or channel environment, equipment type(of the mobile devices), wireless signal power level (e.g., transmissionpower of individual mobile devices, which may be affected by batterypower levels) and/or other parameters. In an embodiment, the data (e.g.,RSSI data) may be represented by a matrix or other data representationto represent data for wireless signals transmitted and received by eachpair (or some subset of the pairs in total) of nodes.

The locations of the nodes are determined from the connection data 230.In an embodiment, the relative locations of the nodes may be determinedusing a force directed graph drawing based on the connection data (e.g.,RSSI data) in the matrix or other data representation. In anotherembodiment, the relative locations may be estimated by minimizing theerror based on the connection data (e.g., through data analysis, usingartificial intelligence, or by other methods).

FIG. 3 shows a photograph of an exemplary experimental environment setupfor a crowd of mobile devices in sub-meter spacing according to anembodiment.

Referring to FIG. 3, the conduit structure in the setup is capable ofhanging the 22 phones 5 feet off of the ground. This conduit structureused strings attached to paperclips to hang theses mobile devices (e.g.,phones). This setup was used for Test 5, 6-20 as will be describedbelow.

In one experimental example as discussed herein, it can be shown RSSIwas a relatively weak or poor indicator of distance (e.g., the locationutilizing RSSI could not normally achieve sub-meter resolution, forexample, trilateration). In this example, mobile devices were positionby laying these devices with phones screen up in a 36 point half metergrid on the ground. The mobile devices were placed about 0.5 metersapart in a square of about 2.5 meters in side length. In order to betterdetermine whether density of devices could improve accuracy a PVCstructure that mimics our original 36 point half-meter grid but insteadof the phones laying face up on the ground the phones can be suspendedin the air with string and paper clips. We used this apparatus for alltests excepting the first 14.

Some precursor to experiments were conducted with wireless mobilestations, e.g., twenty-two (22) Moto-E 2nd gen Smart Phones where usedin the precursor experiments.

Procedure:

-   -   1. Install an app on every wireless mobile station that allows        the phone to act as both a beacon and receiver for RSSI, making        sure the app logs the RSSI every time the receiver is pinged by        a beacon.    -   2. Create a structure capable of hanging the 22 phones 5 feet        off of the ground. We used a conduit structure which we tied        strings to and attached the strings to the phone.        -   FIG. 3 illustrates the structure used in the experiments.        -   FIG. 4 illustrates an exemplary layout diagram for the phone            layout of experimental test 1.

Example 1:—Test 1: High Versus Low Battery

Overview:

Phones were placed 0.5 meters apart on a grid on the floor of a garagelike room. Phones were placed face up. There were no lights on, notablenoises, people, Bluetooth, Wi-Fi etc in the room. The test ran for 5minutes. This test measured the RSSI of phones with high batterycompared to phones with low battery. This measurement was done bylogging the RSSI for 5 minutes. No phones were placed along the diagonalexcept the beacon placed on A1 as shown in FIG. 4. One side of thediagonal consisted solely of phones with battery greater than 75%, withthe other side consisted only of phones with battery lower than 30% asshown is FIG. 4.

Procedure:

-   -   1: Remove all Bluetooth, Wi-Fi, and radio devices from the room.    -   2: Arrange phones in the pattern demonstrated in FIG. 4 such        that phones marked with L are low battery phones (<30%) and        phones marked with H are high battery phones (>75%).        Additionally, note that in FIG. 4, the lighter grid lines        indicate 0.25 meter intervals while the thicker grid lines are        0.5 meters.    -   3: Turn the beacon on.    -   4: Turn every receiver on and start collecting RSSI pings on        each phone.    -   5: Exit the room.    -   6: Return after 5 minutes.    -   7: Turn the beacon and receivers off if they are still on.    -   8: Log all of the RSSI data.

Conclusion:

Upon comparing the average RSSI of phones with high-battery to theaverage RSSI of phones equally far away from the beacon with low batterywe conclude that the effects of battery on average RSSI is negligibledue to the fact that of the 10 pairs of phones at equal distance 6 ofthe low powered phones had higher RSSI values while 4 of the highpowered phones did.

FIG. 5 illustrates an exemplary layout diagram for the phone layout ofexperimental test 2.

Example 2:—Test 2: Low Versus High Battery

Overview:

This test is identical to test 1 however the location of the phones ismirrored along the diagonal.

Procedure:

-   -   1: Remove all Bluetooth, wifi, and radio devices from the room.    -   2: Arrange phones in the pattern demonstrated in FIG. 5 such        that phones marked with L are low battery phones (<30%) and        phones marked with H are high battery phones (>75%).        Additionally, note that in FIG. 5, the lighter grid lines        indicate 0.25 meter intervals while the thicker grid lines are        0.5 meters.    -   3: Turn the beacon on.    -   4: Turn every receiver on and start collecting RSSI pings on        each phone.    -   5: Exit the room.    -   6: Return after 5 minutes.    -   7: Turn the beacon and receivers off if they are still on.    -   8: Log all of the RSSI data.

Conclusion:

This test provided further validation of Test 2's findings that RSSIvalues recorded by receivers do not vary greatly when the receivers havedifferent battery levels. This conclusion was reached do to the factthat, similar to test 1, of the 10 pairs of high battery and low batteryphones 6 of the high powered phones had higher RSSI values and 4 of thelow powered phone had higher RSSI values.

FIG. 6 illustrates a exemplary layout diagram for the phone layout ofexperimental tests 3-5 and tests 6-20;

Example 3:—Test 3: Grounded Basis

Overview:

Phones were placed 0.5 meters apart on a grid on the floor of a garagelike room. Phones were placed face up. There were no lights on, notablenoises, people, Bluetooth, Wi-Fi etc in the room. The test ran for 5minutes.

Procedure:

-   -   1: Remove all Bluetooth, wife and radio devices from the room.    -   2: Arrange phones in the pattern demonstrated in FIG. 6.        Additionally, note that in FIG. 6, the lighter grid lines        indicate 0.25 meter intervals while the thicker grid lines are        0.5 meters. Phone 2 is the beacon. All other phones are        receivers.    -   3: Turn the beacon on and start collecting RSSI pings on each        phone.    -   4: Turn every receiver on and start collecting RSSI pings on        each phone.    -   5: Exit the room.    -   6: Return after 5 minutes.    -   7: Turn the beacon and receivers off if they are still on.    -   8: Log all of the data.

Conclusion:

Linear regressions of RSSI as a function of distance using the data fromthis test have an R-Squared value of 0.07 (7%) suggesting thatincreasing the distance between a receiver and a beacon has a causal,linear effect on RSSI. Additionally, when eliminating only one outlier(phone 17) the R-Squared value jumps to 0.17 which suggests that oncemechanisms for identifying outliers are implemented accuracy willcontinue to grow at increasing rates.

Example 4:—Test 4: Basis with Tarp

Overview:

This test is the same as test 3 however a thin tarp was placed over thephones.

Procedure:

-   -   1: Remove all Bluetooth, Wi-Fi, and radio devices from the room.    -   2: Arrange phones in the pattern demonstrated in FIG. 6.        Additionally, note that in FIG. 6, the lighter grid lines        indicate 0.25 meter intervals while the thicker grid lines are        0.5 meters. Phone 2 is the beacon. All other phones are        receivers.    -   3: Turn the beacon on.    -   4: Turn every receiver on and start collecting RSSI pings on        each phone.    -   5: Place a thin tarp over all of the phones including the        beacon.    -   6: Exit the room.    -   7: Return after 5 minutes.    -   8: Turn the beacon and receivers off if they are still on.    -   9: Log all of the data.

Conclusion:

When comparing the best-fit lines between test 3 and test 4 the slopechanges by only 0.0082 from 1.4727 to 1.4809 while they interceptchanges by 0.169 from 76.655 to 76.824. These changes are statisticallyinsignificant allowing us to conclude that RSSI is not significantlyimpacted by thin covers on beacons or receivers.

Example 5:—Test 5: Hangar Basis

Overview:

Phones were placed 0.5 meters apart on a grid on the floor of anairplane hangar containing a cabin, fridge, TV etc. Phones were placedface up. There were no lights on, notable noises, people, Bluetooth,Wi-Fi, etc. in the room. The test ran for 5 minutes. The phones wereplaced in the South-East corner of the grid.

Procedure:

-   -   1: Remove all Bluetooth, Wi-Fi and radio devices from the room.    -   2: Arrange phones in the pattern demonstrated in FIG. 6.        Additionally, note that in FIG. 6, the lighter grid lines        indicate 0.25 meter intervals while the thicker grid lines are        0.5 meters. Have the beacon in the south-eastern most corner of        the grid. Phone 2 is the beacon. All other phones are receivers.    -   3: Turn the beacon on.    -   4: Turn every receiver on and start collecting RSSI pings on        each phone.    -   5: Exit the room.    -   6: Return after 5 minutes.    -   7: Turn the beacon and receivers off if they are still on.    -   8: Log all of the data.

Conclusions:

In the new location the slope of the best-fit line increases from 1.4727to 2.8708, a statistically insignificant change. However, the interceptincreases from 76.655 to 81.716. This change in intercept indicates thatwhile distance has similar effects on the Bluetooth signal regardless oflocations, the actual values may vary. Graphically this takes the formof the best-fit lines being parallel but not identical.

Table 1 lists the average RSSI values for tests 1-5.

TABLE 1 Distance From Phone ID Beacon Test 1 Test 2 Test 3 Test 4 Test 51 1 77.42 78.49 80.45 75.74 80.81 3 1.414213562 79.98 77.12 77.16 77.494 2 82.78 78.41 76.23 76.44 85.95 5 1.802775638 87.03 79.55 82.32 82.8689.57 6 1.802775638 81.21 75.93 80.63 78.25 86.26 7 1.5 81.53 79.53 8380.55 90.12 8 0.707106781 80.65 77.34 74.18 76.53 9 2.061552813 81.8983.97 84.47 87.79 10 1.58113883 80.34 78.86 79.16 80.53 88.21 11 1 82.3577.73 75.52 89.99 12 1.5 70.2 75.01 80.4 82.35 89.01 13 0.5 79.2 84.8784.5 72.44 14 2 77.9 80.59 79.83 78.98 86 15 1.58113883 85.47 80.8974.48 75.04 88.15 17 2.5 87.13 79.53 80.52 80.78 88.28 18 1.11803398980.43 78.42 76.4 19 0.5 76.42 68.62 75.66 71.22 89.6 20 1.11803398982.25 76.18 82.88 82.81 82.36 21 2.061552813 83.4 82.46 78.89 79.1385.51 22 2.5 77.7 76.27 75.86 76.29 86.82

Example 6:—Test 6: Beacon in SW Corner

Overview:

Test 5 however the beacon was in the SW corner of the grid.

Procedure:

-   -   1: Remove all Bluetooth, Wi-Fi and radio devices from the room.    -   2: Arrange phones in the pattern demonstrated in FIG. 6.        Additionally, note that in FIG. 6, the lighter grid lines        indicate 0.25 meter intervals while the thicker grid lines are        0.5 meters. Have the beacon in the south-western most corner of        the grid. Phone 2 is the beacon. All other phones are receivers.    -   3: Turn the beacon on.    -   4: Turn every receiver on and start collecting RSSI pings on        each phone.    -   5: Exit the room.    -   6: Return after 5 minutes.    -   7: Turn the beacon and receivers off if they are still on.    -   8: Log all of the data.

Conclusion:

With a 7.921 increase in the y-intercept of the best-fit line from test5 to test 6 there appears to be a significant change between testshowever, after eliminating phone 19, an obvious outlier, the differencebetween intercepts decreases to 4.8, a negligible difference, allowingus to infer that orientation within a setting doesn't affect RSSI.

Example 7:—Test 7: Beacon in NW Corner

Overview:

Test 5 however the beacon was in the NW corner of the grid.

Procedure:

-   -   1: Remove all Bluetooth, wife and radio devices from the room.    -   2: Arrange phones in the pattern demonstrated in FIG. 6.        Additionally, note that in FIG. 6, the lighter grid lines        indicate 0.25 meter intervals while the thicker grid lines are        0.5 meters. Have the beacon in the north-western most corner of        the grid. Phone 2 is the beacon. All other phones are receivers.    -   3: Turn the beacon on.    -   4: Turn every receiver on and start collecting RSSI pings on        each phone.    -   5: Exit the room.    -   6: Return after 5 minutes.    -   7: Turn the beacon and receivers off if they are still on.    -   8: Log all of the data.

Conclusion:

With a 3.92 increase in the y-intercept of the best-fit line from test 6to test 7 we extend our inference from example 7 continuing to inferthat orientation within a setting doesn't effect RSSI.

Example 8:—Test 8: Beacon in NE Corner

Overview:

Test 5 however the beacon was in the NE corner of the grid.

Procedure:

-   -   1: Remove all Bluetooth, Wi-Fi and radio devices from the room.    -   2: Arrange phones in the pattern demonstrated in FIG. 6.        Additionally, note that in FIG. 6, the lighter grid lines        indicate 0.25 meter intervals while the thicker grid lines are        0.5 meters. Have the beacon in the north-eastern most corner of        the grid. Phone 2 is the beacon. All other phones are receivers.    -   3: Turn the beacon on.    -   4: Turn every receiver on and start collecting RSSI pings on        each phone.    -   5: Exit the room.    -   6: Return after 5 minutes.    -   7: Turn the beacon and receivers off if they are still on.    -   8: Log all of the data.

Conclusion:

With a 1.28 decrease in the y-intercept of the best-fit line from test 7to test 8 we further extend our inference from example 7 concluding thatorientation within a setting doesn't affect RSSI.

Example 9:—Test 9: Outdoors

Overview:

Test 5 however the phones were on a tarp on asphalt outside.

Procedure:

-   -   1: Arrange phones in the pattern demonstrated in FIG. 6 outside        on top of a tarp placed on top of asphalt. Additionally, note        that in FIG. 6, the lighter grid lines indicate 0.25 meter        intervals while the thicker grid lines are 0.5 meters. Have the        beacon in the south-eastern most corner of the grid. Phone 2 is        the beacon. All other phones are receivers.    -   2: Turn the beacon on.    -   3: Turn every receiver on and start collecting RSSI pings on        each phone.    -   4: Move to where neither beacon nor the receivers are visible.    -   5: Return after 5 minutes.    -   6: Turn the beacon and receivers off if they are still on.    -   7: Log all of the data.

Conclusion:

With a 6.42 increase in the y-intercept from test 5 to test 9 it isevident that RSSI values are different inside versus outside which isconsistent with the conclusion of test 5. Also similar to the conclusionof test 5 RSSI as a function of distance appears to remain linear with asimilar slope.

Example 10:—Test 10: On Floor with Lights on, Loud Music and People

Overview:

Test 5 however lights were on, loud music was playing and three peoplewere circling the grid making noise.

Procedure:

-   -   1: Turn on loud music    -   2: Arrange phones in the pattern demonstrated in FIG. 6.        Additionally, note that in FIG. 6, the lighter grid lines        indicate 0.25 meter intervals while the thicker grid lines are        0.5 meters. Have the beacon in the south-eastern most corner of        the grid. Phone 2 is the beacon. All other phones are receivers.    -   3: Turn the beacon on.    -   4: Turn every receiver on and start collecting RSSI pings on        each phone.    -   5: Move around the grid on skateboards, scooters or some other        loud wheeled vehicle.    -   6: Turn the beacon and receivers off if they are still on.    -   7: Log all of the data.

Conclusion:

Because the slope increases by only 0.82 and the y-intercept decreasesby only 0.99 when compared to test 5 we conclude that noises, lights,and people in the room does not affect RSSI.

Example 11:—Test 11: Hanging Basis

Overview:

Test 5 however instead of on the ground the phones were hung from a PVCapparatus attached via binder clips. The phones were in the sameposition relative to other objects in the hangar, simply elevatedapproximately 5.5-6 feet.

Procedure:

-   -   1: Remove all Bluetooth, wife and radio devices from the room.    -   2: Elevate phones 5 feet off the ground using the hanging        structure described in Precursor to Experiments Procedure step 2        in the pattern demonstrated in FIG. 6. Additionally, note that        in FIG. 6, the lighter grid lines indicate 0.25 meter intervals        while the thicker grid lines are 0.5 meters. Orient the grid so        the beacon is in the South-East most corner of it. Phone 2 is        the beacon. All other phones are receivers.    -   3: Turn the beacon on.    -   4: Turn every receiver on and start collecting RSSI pings on        each phone.    -   5: Exit the room.    -   6: Return after 5 minutes.    -   7: Turn the beacon and receivers off if they are still on.    -   8: Log all of the data.

Conclusion:

In the air there remains a strong linear correlation between RSSI anddistance. However, the y-Intercept decreases by approximately 38 whencompared to test 5. The slopes of the linear regressions for RSSI as afunction of distance in the air vs on the ground remain similar with adifference of only 7.2.

Table 2 lists the average RSSI values for tests 6-11.

TABLE 2 Distance Phone From Test Test Test ID Beacon 6 Test 7 Test 8Test 9 10 11 1 1 76.49 80.34 79.18 82.94 80.22 55.36 3 1.414213562 4 2 51.802775638 88.36 96.23 91.51 99.5 90.64 62.97 6 1.802775638 85.79 88.1591.09 94.85 89.08 59.12 7 1.5 83.32 86.54 82.86 90.15 88.02 55.11 80.707106781 85.61 87.32 83.42 91.18 85.87 48.36 9 2.061552813 87.9186.92 94.9 97.61 88.3 10 1.58113883 84.36 84.66 82.06 92.43 89.46 62.7211 1 82.48 86.82 85.33 95.07 88.44 55.09 12 1.5 13 0.5 69.25 71.56 72.0373.24 73.51 42.79 14 2 90.34 89.67 90.31 93.03 84.89 64.84 15 1.5811388317 2.5 90.02 92.22 88.43 101 90.52 67.6 18 1.118033989 81.27 89.16 90.0891.25 87.12 50.88 19 0.5 79.11 80.95 82.21 86.6 86.09 47.88 201.118033989 82.72 84.16 83.88 85.38 83.12 52.63 21 2.061552813 90.8 95.189.08 102.03 88.22 67.21 22 2.5 87.41 87.97 91.78 96.25 85.96 69.71

Example 12:—Test 12: Hanging with Music

Overview:

Test 11 with loud music playing.

Procedure:

-   -   1: Remove all Bluetooth, wife and radio devices from the room.    -   2: Elevate phones 5 feet off the ground using the hanging        structure described in Precursor to Experiments Procedure step 2        in the pattern demonstrated in FIG. 6. Additionally, note that        in FIG. 6, the lighter grid lines indicate 0.25 meter intervals        while the thicker grid lines are 0.5 meters. Orient the grid so        the beacon is in the South-East most corner of it. Phone 2 is        the beacon. All other phones are receivers.    -   3: Turn on loud music adjacent to the beacon    -   4: Turn the beacon on.    -   5: Turn every receiver on and start collecting RSSI pings on        each phone.    -   6: Exit the room.    -   7: Return after 5 minutes.    -   8: Turn the beacon and receivers off if they are still on.    -   9: Log all of the data.

Conclusion:

Because the Y intercept decreases by only 1.39 when compared to test 5and the slope changes by only 0.87 when compared to test 11 we concludethat the effects of music/noise on RSSI is negligible.

Example 13:—Test 13: Hanging with a Person Walking Around

Overview:

Test 11 with a person walking in circles around the grid.

Procedure:

-   -   1: Remove all Bluetooth, wifi and radio devices from the room.    -   2: Elevate phones 5 feet off the ground using the hanging        structure described in Precursor to Experiments Procedure step 2        in the pattern demonstrated in FIG. 6. Additionally, note that        in FIG. 6, the lighter grid lines indicate 0.25 meter intervals        while the thicker grid lines are 0.5 meters. Orient the grid so        the beacon is in the South-East most corner of it. Phone 2 is        the beacon. All other phones are receivers.    -   3: Turn the beacon on.    -   4: Turn every receiver on and start collecting RSSI pings on        each phone.    -   5: Walk in circles around the grid at a medium pace.    -   6: Turn the beacon and receivers off if they are still on after        5 minutes.    -   7: Log all of the data.

Conclusion:

Because the y intercept decreased by 2.5 and the slope increased by only1.2 for the best fit lines compared to test 11 we conclude that peoplenear the phones has no effect on RSSI.

Example 14:—Test 14: Hanging with a Person in the Grid

Overview:

Test 11 with a person standing on the grid on the point C3.

Procedure:

-   -   1: Remove all Bluetooth, wifi and radio devices from the room.    -   2: Elevate phones 5 feet off the ground using the hanging        structure described in Precursor to Experiments Procedure step 2        in the pattern demonstrated in FIG. 6. Additionally, note that        in FIG. 6, the lighter grid lines indicate 0.25 meter intervals        while the thicker grid lines are 0.5 meters. Orient the grid so        the beacon is in the South-East most corner of it. Phone 2 is        the beacon. All other phones are receivers.    -   3: Turn the beacon on.    -   4: Turn every receiver on and start collecting RSSI pings on        each phone.    -   5: Stand on the grid on the point C3 (depicted in FIG. 6)    -   6: Turn the beacon and receivers off if they are still on after        5 minutes.    -   7: Log all of the data.

Conclusion:

Because they intercept decreased by only 3.9 while the slope increasedby only 1.2 when compared to the best fit line of test 11 we concludethat having human obstruction of the Bluetooth has negligible effects.

Example 15:—Test 15: Hanging with Texting

Overview:

Test 11 with a person standing directly behind the beacon texting on aseparate phone.

Procedure:

-   -   1: Remove all Bluetooth, wife and radio devices from the room        with the exception of your phone.    -   2: Elevate phones 5 feet off the ground using the hanging        structure described in Precursor to Experiments Procedure step 2        in the pattern demonstrated in FIG. 6. Additionally, note that        in FIG. 6, the lighter grid lines indicate 0.25 meter intervals        while the thicker grid lines are 0.5 meters. Orient the grid so        the beacon is in the South-East most corner of it. Phone 2 is        the beacon. All other phones are receivers.    -   3: Turn the beacon on.    -   4: Turn every receiver on and start collecting RSSI pings on        each phone.    -   5: Stand directly behind the beacon    -   6: Text and call people on your phone consistently for 5        minutes.    -   7: Turn the beacon and receivers off if they are still on.    -   8: Log all of the data.

Conclusion:

Because the y intercept decreased by only 4.2 while the slope increasedby only 2.2 when compared to the best fit line of test 11 we concludethat having human stand behind the beacon and cell phone signals do notaffect the RSSI values.

Example 16:—Test 16: Hanging: Beacon in SW Corner

Overview:

Test 11 with the beacon in the South West corner of the grid.

Procedure:

-   -   1: Remove all Bluetooth, wifi and radio devices from the room.    -   2: Elevate phones 5 feet off the ground using the hanging        structure described in Precursor to Experiments Procedure step 2        in the pattern demonstrated in FIG. 6. Additionally, note that        in FIG. 6, the lighter grid lines indicate 0.25 meter intervals        while the thicker grid lines are 0.5 meters. Orient the grid so        the beacon is in the South-West most corner of it. Phone 2 is        the beacon. All other phones are receivers.    -   3: Turn the beacon on.    -   4: Turn every receiver on and start collecting RSSI pings on        each phone.    -   5: Exit the room.    -   6: Return after 5 minutes.    -   7: Turn the beacon and receivers off if they are still on.    -   8: Log all of the data.

Conclusion:

While the slope decreased by 4.9 and the y intercept increased by 10.3when compared to test 11 these changes are insignificant compared to thelarge average RSSI values of the two graphs ranging from 45-70.

Example 17:—Test 17: Hanging: Beacon in NW Corner

Overview:

Test 11 with the beacon in the North-West corner of the grid.

Procedure:

-   -   1: Remove all Bluetooth, wifi and radio devices from the room.    -   2: Elevate phones 5 feet off the ground using the hanging        structure described in Precursor to Experiments Procedure step 2        in the pattern demonstrated in FIG. 6. Additionally, note that        in FIG. 6, the lighter grid lines indicate 0.25 meter intervals        while the thicker grid lines are 0.5 meters. Orient the grid so        the beacon is in the North-West most corner of it. Phone 2 is        the beacon. All other phones are receivers.    -   3: Turn the beacon on.    -   4: Turn every receiver on and start collecting RSSI pings on        each phone.    -   5: Exit the room.    -   6: Return after 5 minutes.    -   7: Turn the beacon and receivers off if they are still on.    -   8: Log all of the data.

Conclusion:

While the slope decreased by 1.7 and they intercept increased by 3.5when compared to test 11 these changes are insignificant compared to thelarge average RSSI values of the two graphs ranging from 45-70.

Example 18:—Test 18: Hanging: Beacon in NE Corner

Overview:

Test 11 with the beacon in the North-East corner of the grid.

Procedure:

-   -   1: Remove all Bluetooth, wifi and radio devices from the room.    -   2: Elevate phones 5 feet off the ground using the hanging        structure described in Precursor to Experiments Procedure step 2        in the pattern demonstrated in FIG. 6. Additionally, note that        in FIG. 6, the lighter grid lines indicate 0.25 meter intervals        while the thicker grid lines are 0.5 meters. Orient the grid so        the beacon is in the North-East most corner of it. Phone 2 is        the beacon. All other phones are receivers.    -   3: Turn the beacon on.    -   4: Turn every receiver on and start collecting RSSI pings on        each phone.    -   5: Exit the room.    -   6: Return after 5 minutes.    -   7: Turn the beacon and receivers off if they are still on.    -   8: Log all of the data.

Conclusion:

While the slope decreased by 3.1 and they intercept increased by 6.2when compared to test 11 these changes are insignificant compared to thelarge average RSSI values of the two graphs ranging from 45-70.

Table 3 lists the Max RSSI value record on each phone for experimentaltests 11-18.

TABLE 3 Distance Phone From Test Test Test Test Test Test Test Test IDBeacon 11 12 13 14 15 16 17 18 13 0.5 40 41 40 43 41 56 45 42 19 0.5 4445 44 47 46 47 47 48 8 0.70710678 46 50 49 50 51 51 50 75 11 1 50 51 5152 52 57 56 49 1 1 51 52 53 52 50 52 53 50 18 1.11803399 48 48 46 48 4751 46 49 20 1.11803399 50 49 54 54 56 52 48 49 7 1.5 52 52 55 54 55 5455 53 10 1.58113883 60 59 58 54 57 57 54 55 5 1.80277564 57 58 60 63 5962 64 58 6 1.80277564 54 54 56 57 58 62 59 58 21 2.06155281 63 57 55 5556 59 61 64 14 2 59 61 56 57 54 65 58 58 17 2.5 63 62 60 61 61 61 62 6322 2.5 66 65 63 63 66 61 59 60

Table 4 lists the minimum RSSI value recorded on each phone for tests11-18.

TABLE 4 Distance Phone From Test Test Test Test Test ID Beacon Test 11Test 12 Test 13 14 15 16 17 18 13 0.5 40 41 40 43 41 56 45 42 19 0.5 4445 44 47 46 47 47 48 8 0.70710678 46 50 49 50 51 51 50 75 11 1 50 51 5152 52 57 56 49 1 1 51 52 53 52 50 52 53 50 18 1.11803399 48 48 46 48 4751 46 49 20 1.11803399 50 49 54 54 56 52 48 49 7 1.5 52 52 55 54 55 5455 53 10 1.58113883 60 59 58 54 57 57 54 55 5 1.80277564 57 58 60 63 5962 64 58 6 1.80277564 54 54 56 57 58 62 59 58 21 2.06155281 63 57 55 5556 59 61 64 14 2 59 61 56 57 54 65 58 58 17 2.5 63 62 60 61 61 61 62 6322 2.5 66 65 63 63 66 61 59 60

Table 5 lists the range between the minimum and maximum values of RSSIin tests 11-18.

TABLE 5 Distance Phone From Test Test Test Test Test ID Beacon Test 11Test 12 Test 13 14 15 16 17 18 13 0.5 6 5 6 4 5 11 4 4 19 0.5 6 5 6 4 69 7 6 8 0.70710678 6 4 3 5 6 6 6 13 1 1 9 7 10 9 10 8 7 6 11 1 11 12 1216 13 4 35 32 18 1.11803399 6 6 9 9 8 7 8 7 20 1.11803399 4 8 8 18 11 67 6 7 1.5 7 10 12 10 10 16 8 10 10 1.58113883 10 12 8 13 7 13 12 12 51.80277564 18 25 28 30 25 13 9 16 6 1.80277564 19 12 20 13 13 7 20 17 212.06155281 13 10 15 13 12 8 13 12 14 2 14 17 14 27 7 21 13 6 17 2.5 2110 14 34 15 19 18 17 22 2.5 14 33 21 17 17 11 10 11

Table 6 lists the average RSSI values for tests 11-18.

TABLE 6 Distance Phone From Test Test Test Test Test Test Test ID Beacon12 13 14 15 16 17 18 1 1 55.56 56.71 57.54 56.27 55.43 55.57 53.29 31.414213562 4 2 5 1.802775638 65.98 68.86 70.54 67.41 67.51 68.02 65.416 1.802775638 58.57 62.91 62.1 62.55 66.08 67.39 65.61 7 1.5 55.77 59.5759.51 60.06 62.08 58.74 56.58 8 0.707106781 51.83 50.75 52.07 52.6553.74 53.7 53.55 9 2.061552813 10 1.58113883 62.16 61.94 62.08 60.2463.37 58.06 59.04 11 1 55.05 55.48 58.03 57.08 58.87 66.81 60.35 12 1.513 0.5 43.26 42.81 44.82 42.99 60.81 47.07 44.18 14 2 65.47 62.29 65.6857.99 70.64 62.61 61.1 15 1.58113883 17 2.5 66.65 66.04 70.41 67.8965.88 68.79 70.81 18 1.118033989 50.74 50.89 51.51 51.51 53.69 49.7752.08 19 0.5 47.39 47.37 49.04 49.11 52.46 51.12 50.99 20 1.11803398953.87 57.84 59.69 61.81 54.52 50.76 51.69 21 2.061552813 60.99 60.860.81 60.58 62.53 65.22 68.66 22 2.5 70.9 69.42 69.25 70.16 64.79 63.0964.14

Example 19:—Test 19: Hanging Outside with Beacon in NE Corner

Overview:

Test 11 located outside with wifi and Bluetooth devices running but atlarge distance from devices (20 yards).

Procedure:

-   -   1: Elevate phones 5.5 feet off the ground using the hanging        structure described in Precursor to Experiments Procedure step 2        in the pattern demonstrated in FIG. 6. Place the hanging        apparatus outside. Additionally, note that in FIG. 6, the        lighter grid lines indicate 0.25 meter intervals while the        thicker grid lines are 0.5 meters. Orient the grid so the beacon        is in the North-East most corner of it. All phones are beacons        and receivers.    -   2: Turn on phones and start collecting RSSI pings on each phone.    -   3: Move to 20 yards from beacons and receivers.    -   4: Return after 5 minutes.    -   5: Turn the beacon and receivers off if they are still on.    -   6: Log all of the data.    -   7: Send raw data (Table 7) through max filtered algorithm as        shown below in max filtered pseudocode to output edges (Table        12).    -   8: Input edges into D3.js force directed algorithm and collect        phone (X,Y) output as shown in Table 13.    -   9: Input (X,Y) values into a scaling algorithm as shown in Max        Filter Psuedocode part IV    -   10: Compare accuracy between test layout and scaled output of        force directed drawing as shown in Table 18.

Conclusion:

Based upon Table 18, the force directed scaled output has a 100% submeter accuracy as well as nearly perfect sequencing accuracy.

Table 7 lists an example Test 19 RSSI Values Hash for signals from Phone11 (ex. First Pairing Phone 10 receiving RSSI from Phone 11, etc.).

TABLE 7 ‘10’: [−58, −58, −76, −63, −58, −63, −64, −58, −59, −70, −65,−61, −60, −66, −62, −75, −62, −67, −58, −64, −64, −65, −60, −63, −60,−61, −60, −59, −57, −59, −63, −72, −70, −62, −62, −62, −58, −66, −64,−63, −56, −62, −68, −63, −65, −59, −60, −66, −64, −57, ‘13’: [−50, −68,−54, −52, −60, −52, −57, −52, −52, −53, −58, −49, −55, −57, −50, −51, −65, −52, −54, −62, −55, −50, −52, −66, −53, −60, −59, −62, −57, −59,−55, −59, −58, −55, −56, −51, −61, −54, −60, −58, −55, −50, −54, −59,−51, −55, −54, −52, −53, −51], ‘12’: [−51, −50, −49, −55, −52, −54, −57,−49, −49, −43, −46, −50, −47, −50, −59, −57, −49, −50, −61, −50, −52,−60, −56, −57, −47, −54, −63, −46, −51, −50, −45, −49, −60, −51, −53,−52, −49, −50, −43, −44, −47, −56, −55, −52, −50, −52, −52, −61, −50,−52, −49, −59, −59, −53, −46, −45, −50, −50], ‘15’: [−62, −59, −67, −56,−56, −52, −53, −51, −58, −59, −58, −52, −54, −51, −55, −56, −53, −54,−51, −57, −51, −55, −52, −52, −52, −55, −53, −51, −53, −68, −50, −54,−55, −54, −57, −51, −51, −50, −52, −50, −50, −53, −57, −55, −60, −48,−55, −55, −54, −57, −56, −58, −52, −68, −54, −54, −60], ‘21’: [−62, −55,−56, −52, −53, −54, −70, −55, −54, −50, −56, −55, −54, −52, −52, −54,−58, −54, −64, −53, −55, −58, −55, −55, −54, −55, −50, −57, −56, −57,−54, −53, −56, −55, −54, −53, −55, −62, −58, −50, −54, −50, −71, −58,−53, −53], ‘17’: [−61, −59, −60, −60, −66, −56, −59, −55, −55, −59, −62,−61, −61, −63, −62, −65, −58, −66, −63, −59, −63, −64, −58, −67, −56,−61, −64, −58, −64, −63, −62, −64, −65, −66, −72, −59, −64, −58, −58,−60, −57, −60, −63, −59, −65, −60, −60, −66, −64, −56, −62, −60, −67,−59, −59, −63, −66], ‘19’: [−52, −50, −51, −65, −46, −46, −48, −50, −51,−52, −50, −48, −54, −48, −52, −68, −45, −58, −52, −44, −44, −51, −49,−62, −45, −48, −54, −47, −50, −55, −57, −54, −50, −52, −58, −55, −48,−49, −52, −47, −47, −44, −49, −52, −51, −50, −57, −44, −52, −53, −50],‘18’: [−52, −53, −46, −48, −48, −51, −49, −51, −47, −49, −48, −50, −48,−48, −49, −45, −50, −52, −46, −57, −47, −52, −45, −48, −50, −46, −50,−45, −44, −50, −49, −54, −48, −46, −49, −48, −44, −65, −49, −50, −54,−50, −46, −49, −46, −49, −51, −47, −46, −44, −51, −65, −52, −46], ‘22’:[−83, −64, −68, −62, −60, −63, −60, −59, −67, −58, −63, −59, −60, −60,−63, −64, −60, −66, −62, −58, −74, −60, −65, −64, −59, −55, −68, −58,−68, −68, −59, −65, −60, −66, −62, −62, −58, −60, −60, −61, −60, −63,−69, −60, −60, −61, −60, −57, −65, −62, −56, −61, −66, −64, −60, −60,−58, −64], ‘1’: [−58, −52, −54, −66, −54, −62, −56, −55, −63, −54, −57,−54, −56, −54, −56, −59, −63, −52, −58, −50, −52, −63, −70, −54, −55,−54, −62, −57, −56, −55, −51, −65, −64, −57, −59, −59, −60, −51, −64,−54, −52, −57, −58, −55, −59, −52, −60, −54, −63, −55, −64], ‘3’: [−57,−59, −55, −54, −56, −66, −66, −56, −63, −63, −68, −61, −56, −64, −65,−62, −61, −77, −60, −65, −62, −57, −59, −59, −52, −62, −56, −64, −62,−58, −60, −63, −56, −59, −57, −53, −55, −58, −54, −63, −58, −61, −62,−60, −62, −63, −60], ‘2’: [−52, −56, −54, −54, −59, −60, −59, −52, −60,−56, −68, −68, −64, −52, −59, −58, −64, −58, −64, −63, −64, −57, −59,−60, −59, −51, −54, −52, −52, −56, −60, −58, −60, −59, −58, −58, −60,−54, −58, −53, −54, −59, −66, −66, −64, −54, −66, −53, −57], ‘5’: [−56,−63, −58, −62, −64, −55, −56, −60, −53, −54, −65, −62, −60, −60, −55,−55, −63, −53, −55, −53, −58, −56, −58, −55, −53, −55, −53, −55, −58,−74, −53, −58, −60, −55, −59, −56, −63, −52, −54, −56, −55, −58, −53,−50, −54, −57, −60, −58, −55, −50], ‘4’: [−60, −55, −55, −57, −53, −54,−61, −57, −53, −56, −53, −60, −56, −58, −58, −60, −59, −61, −57, −61,−54, −52, −58, −59, −59, −58, −55, −62, −66, −56, −59, −56, −58, −58,−61, −50, −52, −62, −50, −53, −58, −61, −59, −56, −58, −59, −61, −54,−60, −53, −60, −53, −55, −56, −58], ‘7’: [−61, −72, −69, −68, −72, −62,−60, −57, −61, −64, −62, −65, −58, −79, −62, −66, −62, −72, −70, −58,−61, −66, −58, −66, −70, −64, −72, −59, −62, −59, −66, −66, −70, −58,−57, −67, −68, −62, −66, −61, −62, −73, −61, −65, −58, −60, −71, −61,−70, −79, −83, −73, −57], ‘6’: [−58, −70, −58, −66, −68, −58, −60, −54,−66, −56, −64, −60, −60, −76, −62, −59, −58, −64, −58, −60, −58, −57,−60, −57, −61, −57, −57, −56, −70, −57, −57, −54, −61, −56, −67, −56,−57, −55, −55, −58, −60, −60, −54, −58, −64], ‘9’: [−65, −80, −65, −62,−61, −65, −60, −73, −75, −63, −65, −62, −76, −72, −69, −73, −74, −70,−74, −70, −64, −63, −62, −67, −68, −66, −60, −63, −66, −62, −74, −58,−68, −65, −70, −77, −71, −92, −70, −65, −64, −63, −63, −62, −59, −66,−60, −59, −68, −68, −67, −63, −82], ‘20’: [−48, −53, −54, −50, −58, −54,−52, −73, −52, −67, −60, −47, −62, −52, −56, −57, −59, −49, −51, −50,−62, −52, −52, −61, −58, −53, −56, −61, −60, −55, −55, −50, −52, −48,−55, −54, −58, −51, −53, −61, −48, −54, −61, −56, −64, −62, −50], ‘8’:[−55, −50, −49, −55, −51, −50, −49, −52, −57, −55, −52, −50, −56, −57,−50, −50, −52, −51, −54, −57, −52, −50, −50, −52, −50, −51, −46, −58,−53, −54, −55, −52, −56, −52, −49, −51, −57, −48, −49, −59, −57, −59,−51, −57, −52, −53, −50], ‘14’: [−64, −70, −62, −56, −61, −56, −58, −69,−69, −68, −69, −60, −56, −67, −62, −63, −56, −57, −57, −56, −54, −60,−61, −58, −55, −66, −57, −59, −63, −55, −57, −62, −56, −55, −53, −54,−57, −54, −64, −82, −71, −57] }, . . .

FIG. 7 illustrates an exemplary diagram for Determining Phone to PhoneRSSI Connection Strength with Kalman Filter in Test 19 according to anembodiment.

Referring to FIG. 7, the RSSI value for the connection between node 1 tonode 20 may be −51.03, and the RSSI value for the connection betweennode 20 to node 1 may be −50.76. Here, the RSSI value may be normalizedto be −50.76 for a determination for both connections (the RSSI value(and distance) between nodes 1 and 20 may be considered the same).

Table 8 lists the Kalman Filtered RSSI Value (One Direction, OtherDirection, Average of Both Directions) for Test 19.

TABLE 8 {(‘1’, ‘20’): [−51.031945442416912, −50.492785341942813,−50.762365392179859], (‘15’, ‘14’): [−61.041343047472253,−62.431630996425206, −61.736487021948733], (‘3’, ‘15’):[−55.141864758743552, −55.318173656446987, −55.230019207595269], (‘22’,‘6’): [−57.970342741009731, −56.424317043870936, −57.197329892440337],(‘13’, ‘21’): [−65.165977861845377, −66.206290836147474,−65.686134348996433], (‘18’, ‘22’): [−59.867056363370949,−60.393840621710012, −60.130448492540481], (‘22’, ‘21’):[−61.539477020660698, −63.400721008912086, −62.470099014786392], (‘11’,‘10’): [−61.896593541363465, −61.472617420318137, −61.684605480840801],(‘4’, ‘5’): [−57.310183361538726, −58.717868956115325,−58.014026158827022], (‘11’, ‘9’): [−66.180667897587071,−65.60271584490512, −65.891691871246096], (‘7’, ‘1’):[−53.990237319372895, −55.00672416695555, −54.498480743164222], (‘21’,‘17’): [−53.393698291954387, −53.540904337241919, −53.467301314598153],(‘10’, ‘19’): [−58.312460541111193, −59.405345352520605,−58.858902946815903], (‘6’, ‘15’): [−54.828833894933275,−53.70385625622351, −54.266345075578393], (‘6’, ‘5’):[−54.576175417763146, −55.03014094404179, −54.803158180902471], (‘20’,‘13’): [−54.939117020728538, −51.788870514032965, −53.363993767380748],(‘14’, ‘18’): [−60.686613608097275, −62.307281708622277,−61.496947658359773], (‘1’, ‘14’): [−55.277174327419246,−56.885427349849991, −56.081300838634618], (‘22’, ‘19’):[−64.318976599506513, −66.228332219914094, −65.273654409710304], (‘2’,‘1’): [−57.073510106217789, −59.195726302430728, −58.134618204324255],(‘19’, ‘15’): [−53.992898167055387, −52.868018486141324,−53.430458326598355], (‘9’, ‘22’): [−53.834939935637024,−54.760464709266294, −54.297702322451656], (‘14’, ‘2’):[−60.942491018364713, −59.882963487933623, −60.412727253149171], (‘4’,‘1’): [−62.972105938748555, −62.369150192300786, −62.670628065524667],(‘5’, ‘14’): [−63.974830226297861, −61.165603423638309,−62.570216824968085], (‘21’, ‘19’): [−64.137258801545102,−63.401912019206868, −63.769585410375981], (‘7’, ‘5’):[−65.059965496011856, −64.588571276055774, −64.824268386033822], (‘2’,‘3’): [−57.397857362781885, −56.776468144532494, −57.087162753657189],(‘20’, ‘18’): [−53.183948574552154, −54.06019719800539,−53.622072886278772], (‘3’, ‘8’): [−52.054629848100532,−53.774216182494726, −52.914423015297629], (‘17’, ‘10’):[−61.944903980018658, −63.613747899124789, −62.77932593957172], (‘22’,‘12’): [−66.2979913392778, −64.863755358278851, −65.580873348778326],(‘10’, ‘21’): [−61.2617531261821, −60.86213507967846,−61.061944102930283], (‘7’, ‘4’): [−68.086156924374166,−66.200089638886894, −67.143123281630523], (‘17’, ‘20’):[−59.50677524273717, −60.948898410594232, −60.227836826665701], (‘14’,‘20’): [−54.635277363359599, −55.667351482292815, −55.151314422826204],(‘3’, ‘7’): [−56.880504444564394, −55.441652648073074,−56.161078546318734], (‘6’, ‘1’): [−56.693435202722164,−56.794851372753556, −56.744143287737856], (‘2’, ‘15’):[−61.473864674510658, −61.129672526515471, −61.301768600513064], (‘7’,‘17’): [−68.446484821514389, −66.826336618463372, −67.636410719988874],(‘9’, ‘7’): [−59.00549451763159, −57.576717046207882,−58.29110578191974], (‘2’, ‘5’): [−63.919251216783678,−65.056460059101795, −64.487855637942744], (‘4’, ‘10’):[−59.369514462129651, −60.675010151703084, −60.022262306916367], (‘8’,‘17’): [−60.390485930310554, −58.203760202129317, −59.297123066219939],(‘22’, ‘8’): [−59.740081276318328, −59.019688185258993,−59.37988473078866], (‘1’, ‘18’): [−57.181619154750038,−56.903099178906153, −57.042359166828092], (‘3’, ‘22’):[−61.187767430256862, −60.789270677950377, −60.98851905410362], (‘21’,‘4’): [−49.617283833160315, −48.767755145744914, −49.192519489452614],(‘2’, ‘7’): [−61.457819270457613, −62.151987651328788,−61.8049034608932], (‘8’, ‘4’): [−63.126119400236156,−62.492825001181849, −62.809472200709003], (‘3’, ‘4’):[−58.860138629775875, −58.507929582669064, −58.68403410622247], (‘10’,‘8’): [−56.179505355281066, −56.860441057973837, −56.519973206627455],(‘6’, ‘18’): [−57.530888678221778, −57.754056385250195,−57.64247253173599], (‘21’, ‘5’): [−53.17030515080544,−53.464301899317263, −53.317303525061348], (‘1’, ‘22’):[−58.963259700841952, −58.910911513935176, −58.937085607388568], (‘21’,‘15’): [−48.901671601957943, −50.438444067535642, −49.670057834746792],(‘3’, ‘13’): [−55.719270839378005, −53.844567462890879,−54.781919151134446], (‘2’, ‘9’): [−63.527136425877799,−64.21963076630071, −63.873383596089255], (‘1’, ‘11’):[−56.754321865715433, −56.201962251841259, −56.478142058778346], (‘17’,‘6’): [−63.926855905974307, −64.431523995072197, −64.179189950523252],(‘13’, ‘9’): [−61.50033432514882, −62.070973640855371,−61.785653983002092], (‘11’, ‘12’): [−50.836213852380872,−50.830029815757278, −50.833121834069075], (‘6’, ‘21’):[−56.406590862321991, −56.501457643973168, −56.45402425314758], (‘13’,‘18’): [−56.620042920793864, −57.008847226562843, −56.814445073678357],(‘17’, ‘5’): [−59.833090828230908, −59.400169953571663,−59.616630390901285], (‘13’, ‘8’): [−51.365168987758871,−51.589401283762811, −51.477285135760837], (‘4’, ‘9’):[−67.354293999098857, −68.814218640763826, −68.084256319931342], (‘19’,‘8’): [−50.974562369390547, −50.254231198747895, −50.614396784069221],(‘11’, ‘5’): [−57.25709438807182, −58.049064276481772,−57.653079332276796], (‘9’, ‘19’): [−68.286387172021747,−67.654670145340319, −67.970528658681033], (‘12’, ‘18’):[−52.156080539377896, −52.931857338004249, −52.543968938691073], (‘14’,‘12’): [−65.094436902448379, −63.638950029635808, −64.366693466042094],(‘20’, ‘22’): [−60.658463642434555, −62.528960635457466,−61.59371213894601], (‘21’, ‘1’): [−62.841896551226405,−63.194316198626531, −63.018106374926468], (‘13’, ‘6’):[−55.409396522552584, −55.079922457151589, −55.244659489852083], (‘13’,‘11’): [−53.693749514900972, −54.793219871412781, −54.243484693156873],(‘19’, ‘17’): [−66.568355050383587, −65.364464244293316,−65.966409647338452], (‘2’, ‘10’): [−60.686688767023817,−61.088316219477363, −60.887502493250594], (‘4’, ‘6’):[−63.11700468316473, −62.486984780026916, −62.801994731595826], (‘4’,‘17’): [−53.225500538505642, −54.002674122326091, −53.61408733041587],(‘8’, ‘12’): [−54.385513354459306, −54.381778915451733,−54.383646134955519], (‘6’, ‘2’): [−63.245787673615986,−64.274495435618093, −63.760141554617036], (‘22’, ‘10’):[−55.424153164184403, −55.63465054408627, −55.529401854135337], (‘19’,‘5’): [−57.165632370796573, −56.100636490084497, −56.633134430440535],(‘22’, ‘7’): [−56.068292978429078, −56.267160690788018,−56.167726834608544], (‘2’, ‘17’): [−67.655322199399151,−65.338094287797617, −66.496708243598391], (‘7’, ‘15’):[−63.723967007298654, −64.397100813778351, −64.06053391053851], (‘9’,‘18’): [−62.325559914883925, −62.577612042151813, −62.451585978517869],(‘8’, ‘15’): [−54.162237912835081, −55.67383317946377,−54.918035546149426], (‘9’, ‘1’): [−57.901710715383594,−58.64117090704471, −58.271440811214148], (‘14’, ‘19’):[−63.717415266670024, −64.410504027626402, −64.063959647148209], (‘3’,‘18’): [−51.298991304336717, −50.659373423347652, −50.979182363842185],(‘11’, ‘20’): [−54.00651324234547, −52.871883785456866,−53.439198513901168], (‘18’, ‘2’): [−56.309749629273725,−53.948736111712464, −55.129242870493094], (‘4’, ‘2’):[−59.710241021801487, −62.231964996744559, −60.971103009273023], (‘13’,‘1’): [−50.246684386920066, −51.716888939689824, −50.981786663304945],(‘10’, ‘9’): [−52.408701677151491, −53.635596980492792,−53.022149328822138], (‘13’, ‘22’): [−62.52149984526153,−62.86886584857448, −62.695182846918001], (‘3’, ‘20’):[−49.650757585552704, −48.953373735850477, −49.302065660701587], (‘22’,‘4’): [−71.03242192632672, −68.84728240261316, −69.93985216446994],(‘11’, ‘2’): [−56.929624519263847, −55.701890066615221,−56.315757292939537], (‘4’, ‘19’): [−59.362794535021628,−62.704779671923561, −61.033787103472591], (‘21’, ‘8’):[−57.9008267083722, −57.247825696473491, −57.574326202422846], (‘15’,‘10’): [−55.42230130389261, −54.243148078024014, −54.832724690958315],(‘3’, ‘11’): [−59.154888267653497, −59.176829619796585,−59.165858943725041], (‘12’, ‘13’): [−62.497997217332191,−63.238848352502181, −62.868422784917186], (‘8’, ‘2’):[−53.264524562065041, −53.195940126996327, −53.230232344530684], (‘3’,‘9’): [−57.435118769921232, −57.879635031171567, −57.657376900546396],(‘6’, ‘7’): [−59.229849565444553, −58.700057622260204,−58.964953593852378], (‘21’, ‘9’): [−63.56983671192063,−63.062001269727617, −63.315918990824123], (‘11’, ‘14’):[−60.299656937924702, −58.511450955495256, −59.405553946709979], (‘18’,‘11’): [−49.172171879689017, −48.57257576590213, −48.872373822795574],(‘6’, ‘11’): [−61.472555365020661, −60.198984417530589,−60.835769891275625], (‘7’, ‘19’): [−62.589681640485828,−63.796115598989573, −63.192898619737704], (‘17’, ‘3’):[−62.93865788530443, −63.237204283838942, −63.087931084571686], (‘19’,‘2’): [−51.757944277538058, −50.919148396309545, −51.338546336923798],(‘17’, ‘13’): [−65.104720118176317, −66.542654690417621,−65.823687404296976], (‘14’, ‘10’): [−55.110042908845948,−54.422527373866025, −54.766285141355986], (‘12’, ‘21’):[−51.632327826550721, −50.705317107784538, −51.168822467167629], (‘3’,‘6’): [−53.164225714977526, −51.120000045115646, −52.142112880046582],(‘7’, ‘21’): [−61.586027875817777, −60.742942081277583,−61.164484978547677], (‘10’, ‘3’): [−53.20917970969009,−54.667210212403404, −53.938194961046747], (‘2’, ‘12’):[−59.447064747650316, −60.235662387283753, −59.841363567467035], (‘3’,‘5’): [−52.379651118374213, −51.962037973125632, −52.170844545749922],(‘7’, ‘12’): [−64.145308560123624, −64.06169691735559,−64.103502738739607], (‘5’, ‘10’): [−54.725091886530407,−54.853956054569736, −54.789523970550071], (‘9’, ‘15’):[−65.146793867868169, −66.293249255421131, −65.720021561644643], (‘4’,‘15’): [−52.322381851949423, −54.956856136321427, −53.639618994135425],(‘17’, ‘14’): [−68.530946239143006, −67.947643783511083,−68.239295011327044], (‘10’, ‘1’): [−55.033185479575273,−53.391675051680593, −54.21243026562793], (‘11’, ‘8’):[−51.697474826546269, −51.256203031211186, −51.476838928878728], (‘21’,‘2’): [−63.419130411843128, −63.494952173531715, −63.457041292687421],(‘20’, ‘4’): [−61.084348277222411, −61.11532631098202,−61.099837294102215], (‘15’, ‘5’): [−50.425724011824087,−49.583866343640096, −50.004795177732092], (‘11’, ‘22’):[−62.783819587859512, −63.242589811539823, −63.013204699699671], (‘20’,‘9’): [−57.353065202281726, −57.916626686322488, −57.634845944302107],(‘21’, ‘20’): [−60.200238123685352, −60.244668385509591,−60.222453254597468], (‘19’, ‘13’): [−53.768595311807395,−53.071320185504959, −53.419957748656174], (‘6’, ‘14’):[−54.736052442187862, −53.425764503989107, −54.080908473088485], (‘14’,‘3’): [−56.024696858182892, −55.902490460928568, −55.963593659555727],(‘14’, ‘8’): [−62.680536791838982, −62.631010226969146,−62.655773509404064], (‘15’, ‘12’): [−50.543325442339217,−48.960436017965293, −49.751880730152251], (‘1’, ‘5’):[−60.888103207956391, −60.367814874139285, −60.627959041047838], (‘20’,‘10’): [−49.515305940208691, −49.387993539753687, −49.451649739981193],(‘11’, ‘7’): [−63.684398200617352, −64.559447906773926,−64.121923053695639], (‘17’, ‘18’): [−57.447302216305864,−58.016960035214211, −57.732131125760034], (‘1’, ‘15’):[−61.05516744753016, −62.705684597191407, −61.88042602236078], (‘13’,‘14’): [−59.031056533248098, −56.901675994094305, −57.966366263671205],(‘21’, ‘14’): [−64.739971105047573, −65.419869282260265,−65.079920193653919], (‘10’, ‘13’): [−54.299539949659355,−54.712643649689198, −54.50609179967428], (‘19’, ‘12’):[−57.548207758835666, −56.546636838667972, −57.047422298751819], (‘20’,‘2’): [−55.282066013764002, −55.786319691160308, −55.534192852462155],(‘9’, ‘6’): [−56.655356976250872, −56.257071095255334,−56.456214035753106], (‘21’, ‘18’): [−56.181684623712258,−57.438384487364274, −56.810034555538266], (‘20’, ‘15’):[−52.849621783177355, −53.256765105934058, −53.053193444555703], (‘20’,‘19’): [−57.371523896643914, −58.378318109282191, −57.874921002963049],(‘17’, ‘11’): [−58.454033286557845, −60.113852539978993,−59.283942913268419], (‘17’, ‘9’): [−68.847052818123871,−67.203561172149932, −68.025306995136901], (‘22’, ‘5’):[−64.379478227396547, −62.863783354057006, −63.621630790726776], (‘7’,‘10’): [−53.851084481947112, −53.411908057343219, −53.631496269645169],(‘5’, ‘12’): [−57.542709218769865, −56.853554760885018,−57.198131989827445], (‘4’, ‘13’): [−63.299048936269138,−64.100520712989166, −63.699784824629148], (‘22’, ‘14’):[−47.554548223762573, −48.309118251320974, −47.931833237541774], (‘1’,‘19’): [−57.277680070166113, −57.7148519609147, −57.496266015540407],(‘20’, ‘8’): [−48.722132559390602, −49.015827780322503,−48.868980169856556], (‘11’, ‘4’): [−56.351036996599625,−55.067104402699655, −55.709070699649644], (‘15’, ‘17’):[−55.634691195816622, −55.592641933418669, −55.613666564617645], (‘3’,‘1’): [−60.062313923835589, −60.869353673266694, −60.465833798551145],(‘12’, ‘10’): [−62.310567201353614, −61.485174733992899,−61.897870967673256], (‘12’, ‘1’): [−63.654529649957873,−66.352133835888637, −65.003331742923251], (‘18’, ‘7’):[−59.314775576111707, −60.159895056910734, −59.737335316511221], (‘8’,‘6’): [−54.983775084118669, −53.552982025070115, −54.268378554594392],(‘5’, ‘9’): [−61.494902731528285, −61.427918990616824,−61.461410861072551], (‘12’, ‘3’): [−56.486416612709107,−55.882361407998317, −56.184389010353712], (‘12’, ‘17’):[−56.877149545187336, −55.490933862310136, −56.184041703748733], (‘17’,‘1’): [−66.232563690830247, −67.684560051159991, −66.958561870995112],(‘18’, ‘5’): [−54.549524164347488, −53.513712676837969,−54.031618420592729], (‘12’, ‘4’): [−49.73611764007979,−48.467493914726987, −49.101805777403385], (‘18’, ‘15’):[−48.07782417457296, −49.252433716205552, −48.665128945389256], (‘9’,‘8’): [−60.76261053384912, −60.568963783575661, −60.665787158712391],(‘12’, ‘6’): [−60.256544124783254, −60.663231220460425,−60.459887672621839], (‘1’, ‘8’): [−58.039370519221258,−56.233708833261865, −57.136539676241561], (‘10’, ‘18’):[−53.063040833127559, −53.245295456043621, −53.15416814458559], (‘13’,‘7’): [−59.31995203795455, −58.623026130010082, −58.97148908398232],(‘3’, ‘19’): [−56.660687173859799, −55.999350700637422,−56.33001893724861], (‘12’, ‘9’): [−63.806748249427216,−63.359872650552823, −63.583310449990023], (‘20’, ‘12’):[−53.079696346792439, −53.478528110530014, −53.279112228661226], (‘2’,‘22’): [−63.853127348147346, −66.916565592881255, −65.384846470514304],(‘6’, ‘19’): [−61.324571683531957, −63.537851581509557,−62.431211632520757], (‘13’, ‘5’): [−60.860758625988794,−62.253566470492174, −61.557162548240484], (‘4’, ‘18’):[−51.808137140363961, −53.517578467779551, −52.662857804071756], (‘15’,‘11’): [−55.277427226536481, −54.986030537995624, −55.131728882266053],(‘20’, ‘7’): [−53.914222834397137, −53.892091603510266,−53.903157218953702], (‘6’, ‘20’): [−52.850453466323017,−51.669107969392591, −52.259780717857808], (‘5’, ‘8’):[−54.063321594225776, −54.242292296518023, −54.1528069453719], (‘19’,‘11’): [−52.754167792531064, −50.479509992078768, −51.616838892304912],(‘6’, ‘10’): [−49.457358698275669, −49.684828325281835,−49.571093511778756], (‘20’, ‘5’): [−60.281354901692644,−59.60179643010121, −59.941575665896927], (‘13’, ‘2’):[−48.353721437657477, −48.403099173558168, −48.378410305607822], (‘21’,‘11’): [−54.269993215895724, −54.874774659120028, −54.57238393750788],(‘14’, ‘4’): [−63.736211165860453, −63.944364997790636,−63.840288081825548], (‘22’, ‘17’): [−72.061576013981721,−69.156652375935025, −70.60911419495838], (‘7’, ‘14’):[−50.433444369883752, −50.580072080591911, −50.506758225237832], (‘14’,‘9’): [−50.489537114457647, −49.803861575733485, −50.14669934509557],(‘15’, ‘22’): [−63.969829934096843, −66.16105430340906,−65.065442118752955], (‘3’, ‘21’): [−55.35950791884553,−56.294182190641429, −55.82684505474348], (‘8’, ‘7’):[−58.171730502492018, −56.279187820566463, −57.225459161529244], (‘15’,‘13’): [−58.966269899599325, −57.94659373287913, −58.456431816239231],(‘18’, ‘8’): [−48.112918785645675, −47.083772570878097,−47.598345678261886], (‘18’, ‘19’): [−55.609213503122611,−54.109479912772464, −54.859346707947537]}

Table 9 lists the Kalman Filtered Test 19 Output Edges (Connections) tobe inserted into force directed graph theory inputs.

TABLE 9 Edges = [[‘1’, ‘20’], [‘1’, ‘13’], [‘1’, ‘10’], [‘1’, ‘7’],[‘2’, ‘13’], [‘2’, ‘19’], [‘2’, ‘8’], [‘2’, ‘18’], [‘3’, ‘20’], [‘3’,‘18’], [‘3’, ‘6’], [‘3’, ‘5’], [‘3’, ‘8’], [‘4’, ‘12’], [‘4’, ‘21’],[‘4’, ‘18’], [‘4’, ‘17’], [‘5’, ‘15’], [‘5’, ‘3’], [‘5’, ‘21’], [‘5’,‘18’], [‘5’, ‘8’], [‘6’, ‘10’], [‘6’, ‘3’], [‘6’, ‘20’], [‘6’, ‘14’],[‘6’, ‘15’], [‘7’, ‘14’], [‘7’, ‘10’], [‘7’, ‘20’], [‘7’, ‘1’], [‘7’,‘3’], [‘8’, ‘18’], [‘8’, ‘20’], [‘8’, ‘19’], [‘8’, ‘11’], [‘8’, ‘13’],[‘9’, ‘14’], [‘9’, ‘10’], [‘9’, ‘22’], [‘9’, ‘6’], [‘10’, ‘20’], [‘10’,‘6’], [‘10’, ‘9’], [‘10’, ‘18’], [‘10’, ‘7’], [‘11’, ‘18’], [‘11’,‘12’], [‘11’, ‘8’], [‘11’, ‘19’], [‘11’, ‘20’], [‘12’, ‘4’], [‘12’,‘15’], [‘12’, ‘11’], [‘12’, ‘21’], [‘12’, ‘18’], [‘13’, ‘2’], [‘13’,‘1’], [‘13’, ‘8’], [‘13’, ‘20’], [‘13’, ‘19’], [‘14’, ‘22’], [‘14’,‘9’], [‘14’, ‘7’], [‘14’, ‘6’], [‘15’, ‘18’], [‘15’, ‘21’], [‘15’,‘12’], [‘15’, ‘5’], [‘15’, ‘20’], [‘17’, ‘21’], [‘17’, ‘4’], [‘17’,‘15’], [‘17’, ‘12’], [‘18’, ‘8’], [‘18’, ‘15’], [‘18’, ‘11’], [‘18’,‘3’], [‘18’, ‘12’], [‘19’, ‘8’], [‘19’, ‘2’], [‘19’, ‘11’], [‘19’,‘13’], [‘19’, ‘15’], [‘20’, ‘8’], [‘20’, ‘3’], [‘20’, ‘10’], [‘20’,‘1’], [‘20’, ‘6’], [‘21’, ‘4’], [‘21’, ‘15’], [‘21’, ‘12’], [‘21’, ‘5’],[‘22’, ‘14’], [‘22’, ‘9’]]

FIG. 8 illustrates an exemplary diagram for Kalman Filtered ForceDirected Graph Drawing Using Force Directed Graph Drawing Library D3.jsin Test 19.

Table 10 lists the Kalman Filtered (X,Y) coordinate for Test 19.

TABLE 10 Kalman_Filtered_Coordinates =[[“1”,458.5329141222706,94.14750059478128],[“2”,211.94776585069076,−63.35473535607196],[“3”,270.24444573641847,283.6874768374006],[“4”,−109.13671005209524,277.46045059780266],[“5”,138.33486094002419,359.4507208269634],[“6”,377.3524201988945,358.9532472175572],[“7”,520.2057063582507,221.879107553335],[“8”,219.59528966608423,102.64145373542816],[“9”,517.2887496584899,447.98478419203445],[“10”,431.30043654985474,277.7150634858197],[“11”,91.8583783012601,100.67873710172903],[“12”,−15.534089758761343,226.06116151204526],[“13”,313.83175453940163,−6.821709906686433],[“14”,574.9981303642516,370.5214743367375],[“15”,90.7264058940126,286.625914321973],[“17”,−124.26363823739828,371.3823588497608],[“18”,135.46691247472558,201.94905796310925],[“19”,150.20499834986774,4.210532575829226],[“20”,335.87694004917006,187.2467469529189],[“21”,−13.737499217366466,368.22634891341596],[“22”,633.9718126571761,492.7709502855854]]

FIG. 9 illustrates an exemplary diagram for determining phone to phoneconnection strength with max filter in Test 19 according to anembodiment. It is noted that the maxes of the phone to phone connectionstrength is average.

Table 11 lists max values (One Direction, Other Direction, Average ofBoth Directions) for Test 19.

TABLE 11 Max_values = {(‘1’, ‘20’): [−44, −44, −44.0], (‘15’, ‘14’):[−55, −52, −53.5], (‘3’, ‘15’): [−49, −50, −49.5], (‘22’, ‘6’): [−53,−52, −52.5], (‘13’, ‘21’): [−58, −58, −58.0], (‘18’, ‘22’): [−55, −55,−55.0], (‘22’, ‘21’): [−57, −56, −56.5], (‘11’, ‘10’): [−56, −57,−56.5], (‘4’, ‘5’): [−52, −53, −52.5], (‘11’, ‘9’): [−58, −59, −58.5],(‘7’, ‘1’): [−48, −49, −48.5], (‘21’, ‘17’): [−48, −48, −48.0], (‘10’,‘19’): [−54, −53, −53.5], (‘6’, ‘15’): [−49, −50, −49.5], (‘6’, ‘5’):[−50, −50, −50.0], (‘20’, ‘13’): [−48, −47, −47.5], (‘14’, ‘18’): [−52,−54, −53.0], (‘1’, ‘14’): [−51, −52, −51.5], (‘22’, ‘19’): [−58, −58,−58.0], (‘2’, ‘1’): [−52, −52, −52.0], (‘19’, ‘15’): [−48, −48, −48.0],(‘9’, ‘22’): [−47, −48, −47.5], (‘14’, ‘2’): [−55, −55, −55.0], (‘4’,‘1’): [−56, −56, −56.0], (‘5’, ‘14’): [−57, −56, −56.5], (‘21’, ‘19’):[−57, −56, −56.5], (‘7’, ‘5’): [−57, −59, −58.0], (‘2’, ‘3’): [−52, −52,−52.0], (‘20’, ‘18’): [−46, −50, −48.0], (‘3’, ‘8’): [−47, −47, −47.0],(‘17’, ‘10’): [−58, −58, −58.0], (‘22’, ‘12’): [−60, −57, −58.5], (‘10’,‘21’): [−54, −54, −54.0], (‘7’, ‘4’): [−59, −59, −59.0], (‘17’, ‘20’):[−54, −55, −54.5], (‘14’, ‘20’): [−48, −47, −47.5], (‘3’, ‘7’): [−53,−53, −53.0], (‘6’, ‘1’): [−50, −50, −50.0], (‘2’, ‘15’): [−52, −54,−53.0], (‘7’, ‘17’): [−64, −62, −63.0], (‘9’, ‘7’): [−50, −49, −49.5],(‘2’, ‘5’): [−58, −59, −58.5], (‘4’, ‘10’): [−55, −56, −55.5], (‘8’,‘17’): [−55, −53, −54.0], (‘22’, ‘8’): [−52, −53, −52.5], (‘1’, ‘18’):[−50, −50, −50.0], (‘3’, ‘22’): [−54, −55, −54.5], (‘21’, ‘4’): [−43,−43, −43.0], (‘2’, ‘7’): [−55, −54, −54.5], (‘8’, ‘4’): [−56, −55,−55.5], (‘3’, ‘4’): [−54, −52, −53.0], (‘10’, ‘8’): [−50, −50, −50.0],(‘6’, ‘18’): [−52, −52, −52.0], (‘21’, ‘5’): [−48, −48, −48.0], (‘1’,‘22’): [−51, −52, −51.5], (‘21’, ‘15’): [−42, −44, −43.0], (‘3’, ‘13’):[−50, −50, −50.0], (‘2’, ‘9’): [−58, −58, −58.0], (‘1’, ‘11’): [−50,−50, −50.0], (‘17’, ‘6’): [−59, −58, −58.5], (‘13’, ‘9’): [−54, −56,−55.0], (‘11’, ‘12’): [−43, −42, −42.5], (‘6’, ‘21’): [−51, −52, −51.5],(‘13’, ‘18’): [−50, −50, −50.0], (‘17’, ‘5’): [−53, −54, −53.5], (‘13’,‘8’): [−45, −44, −44.5], (‘4’, ‘9’): [−59, −61, −60.0], (‘19’, ‘8’):[−47, −43, −45.0], (‘11’, ‘5’): [−50, −52, −51.0], (‘9’, ‘19’): [−61,−61, −61.0], (‘12’, ‘18’): [−46, −48, −47.0], (‘14’, ‘12’): [−59, −56,−57.5], (‘20’, ‘22’): [−52, −54, −53.0], (‘21’, ‘1’): [−55, −55, −55.0],(‘13’, ‘6’): [−49, −50, −49.5], (‘13’, ‘11’): [−48, −49, −48.5], (‘19’,‘17’): [−59, −59, −59.0], (‘2’, ‘10’): [−55, −53, −54.0], (‘4’, ‘6’):[−54, −56, −55.0], (‘4’, ‘17’): [−46, −46, −46.0], (‘8’, ‘12’): [−48,−48, −48.0], (‘6’, ‘2’): [−58, −58, −58.0], (‘22’, ‘10’): [−47, −50,−48.5], (‘19’, ‘5’): [−51, −50, −50.5], (‘22’, ‘7’): [−53, −52, −52.5],(‘2’, ‘17’): [−62, −61, −61.5], (‘7’, ‘15’): [−57, −58, −57.5], (‘9’,‘18’): [−55, −56, −55.5], (‘8’, ‘15’): [−48, −50, −49.0], (‘9’, ‘1’):[−51, −50, −50.5], (‘14’, ‘19’): [−55, −58, −56.5], (‘3’, ‘18’): [−44,−46, −45.0], (‘11’, ‘20’): [−47, −46, −46.5], (‘18’, ‘2’): [−50, −50,−50.0], (‘4’, ‘2’): [−54, −57, −55.5], (‘13’, ‘1’): [−46, −45, −45.5],(‘10’, ‘9’): [−45, −47, −46.0], (‘13’, ‘22’): [−56, −57, −56.5], (‘3’,‘20’): [−43, −43, −43.0], (‘22’, ‘4’): [−61, −60, −60.5], (‘11’, ‘2’):[−51, −50, −50.5], (‘4’, ‘19’): [−53, −53, −53.0], (‘21’, ‘8’): [−53,−52, −52.5], (‘15’, ‘10’): [−50, −50, −50.0], (‘3’, ‘11’): [−54, −52,−53.0], (‘12’, ‘13’): [−58, −56, −57.0], (‘8’, ‘2’): [−49, −47, −48.0],(‘3’, ‘9’): [−54, −53, −53.5], (‘6’, ‘7’): [−55, −54, −54.5], (‘21’,‘9’): [−59, −57, −58.0], (‘11’, ‘14’): [−53, −53, −53.0], (‘18’, ‘11’):[−44, −44, −44.0], (‘6’, ‘11’): [−55, −54, −54.5], (‘7’, ‘19’): [−55,−57, −56.0], (‘17’, ‘3’): [−55, −57, −56.0], (‘19’, ‘2’): [−44, −43,−43.5], (‘17’, ‘13’): [−59, −61, −60.0], (‘14’, ‘10’): [−48, −46,−47.0], (‘12’, ‘21’): [−47, −47, −47.0], (‘3’, ‘6’): [−47, −46, −46.5],(‘7’, ‘21’): [−55, −54, −54.5], (‘10’, ‘3’): [−49, −49, −49.0], (‘2’,‘12’): [−52, −53, −52.5], (‘3’, ‘5’): [−48, −48, −48.0], (‘7’, ‘12’):[−56, −56, −56.0], (‘5’, ‘10’): [−50, −50, −50.0], (‘9’, ‘15’): [−57,−57, −57.0], (‘4’, ‘15’): [−47, −48, −47.5], (‘17’, ‘14’): [−61, −62,−61.5], (‘10’, ‘1’): [−46, −48, −47.0], (‘11’, ‘8’): [−46, −46, −46.0],(‘21’, ‘2’): [−57, −56, −56.5], (‘20’, ‘4’): [−57, −55, −56.0], (‘15’,‘5’): [−45, −44, −44.5], (‘11’, ‘22’): [−55, −56, −55.5], (‘20’, ‘9’):[−52, −52, −52.0], (‘21’, ‘20’): [−52, −51, −51.5], (‘19’, ‘13’): [−49,−48, −48.5], (‘6’, ‘14’): [−51, −50, −50.5], (‘14’, ‘3’): [−52, −52,−52.0], (‘14’, ‘8’): [−56, −56, −56.0], (‘15’, ‘12’): [−43, −42, −42.5],(‘1’, ‘5’): [−55, −56, −55.5], (‘20’, ‘10’): [−43, −42, −42.5], (‘11’,‘7’): [−57, −56, −56.5], (‘17’, ‘18’): [−54, −55, −54.5], (‘1’, ‘15’):[−54, −55, −54.5], (‘13’, ‘14’): [−52, −52, −52.0], (‘21’, ‘14’): [−59,−59, −59.0], (‘10’, ‘13’): [−50, −50, −50.0], (‘19’, ‘12’): [−52, −53,−52.5], (‘20’, ‘2’): [−47, −46, −46.5], (‘9’, ‘6’): [−51, −52, −51.5],(‘21’, ‘18’): [−52, −54, −53.0], (‘20’, ‘15’): [−48, −49, −48.5], (‘20’,‘19’): [−51, −54, −52.5], (‘17’, ‘11’): [−54, −55, −54.5], (‘17’, ‘9’):[−58, −62, −60.0], (‘22’, ‘5’): [−58, −58, −58.0], (‘7’, ‘10’): [−47,−48, −47.5], (‘5’, ‘12’): [−53, −53, −53.0], (‘4’, ‘13’): [−57, −57,−57.0], (‘22’, ‘14’): [−43, −42, −42.5], (‘1’, ‘19’): [−52, −53, −52.5],(‘20’, ‘8’): [−43, −42, −42.5], (‘11’, ‘4’): [−50, −50, −50.0], (‘15’,‘17’): [−51, −50, −50.5], (‘3’, ‘1’): [−54, −53, −53.5], (‘12’, ‘10’):[−55, −54, −54.5], (‘12’, ‘1’): [−56, −57, −56.5], (‘18’, ‘7’): [−53,−51, −52.0], (‘8’, ‘6’): [−50, −50, −50.0], (‘5’, ‘9’): [−55, −55,−55.0], (‘12’, ‘3’): [−50, −50, −50.0], (‘12’, ‘17’): [−50, −49, −49.5],(‘17’, ‘1’): [−61, −62, −61.5], (‘18’, ‘5’): [−50, −48, −49.0], (‘12’,‘4’): [−43, −42, −42.5], (‘18’, ‘15’): [−42, −42, −42.0], (‘9’, ‘8’):[−53, −54, −53.5], (‘12’, ‘6’): [−53, −55, −54.0], (‘1’, ‘8’): [−49,−50, −49.5], (‘10’, ‘18’): [−48, −48, −48.0], (‘13’, ‘7’): [−52, −52,−52.0], (‘3’, ‘19’): [−50, −51, −50.5], (‘12’, ‘9’): [−57, −55, −56.0],(‘20’, ‘12’): [−48, −48, −48.0], (‘2’, ‘22’): [−58, −59, −58.5], (‘6’,‘19’): [−57, −57, −57.0], (‘13’, ‘5’): [−56, −56, −56.0], (‘4’, ‘18’):[−48, −50, −49.0], (‘15’, ‘11’): [−49, −48, −48.5], (‘20’, ‘7’): [−49,−48, −48.5], (‘6’, ‘20’): [−46, −45, −45.5], (‘5’, ‘8’): [−51, −51,−51.0], (‘19’, ‘11’): [−45, −44, −44.5], (‘6’, ‘10’): [−45, −43, −44.0],(‘20’, ‘5’): [−51, −53, −52.0], (‘13’, ‘2’): [−43, −43, −43.0], (‘21’,‘11’): [−49, −50, −49.5], (‘14’, ‘4’): [−58, −56, −57.0], (‘22’, ‘17’):[−65, −63, −64.0], (‘7’, ‘14’): [−44, −44, −44.0], (‘14’, ‘9’): [−43,−44, −43.5], (‘15’, ‘22’): [−57, −57, −57.0], (‘3’, ‘21’): [−52, −51,−51.5], (‘8’, ‘7’): [−52, −50, −51.0], (‘15’, ‘13’): [−52, −52, −52.0],(‘18’, ‘8’): [−43, −43, −43.0], (‘18’, ‘19’): [−49, −47, −48.0]}

Table 12 lists max filtered Output Edges (Connections) to be insertedinto D3.js code for Test 19.

TABLE 12 Max_edges = [[‘1’, ‘20’], [‘1’, ‘13’], [‘1’, ‘10’], [‘1’, ‘7’],[‘2’, ‘13’], [‘2’, ‘19’], [‘2’, ‘20’], [‘2’, ‘8’], [‘3’, ‘20’], [‘3’,‘18’], [‘3’, ‘6’], [‘3’, ‘8’], [‘3’, ‘5’], [‘4’, ‘12’], [‘4’, ‘21’],[‘4’, ‘17’], [‘4’, ‘15’], [‘5’, ‘15’], [‘5’, ‘21’], [‘5’, ‘3’], [‘5’,‘18’], [‘5’, ‘6’], [‘6’, ‘10’], [‘6’, ‘20’], [‘6’, ‘3’], [‘6’, ‘15’],[‘7’, ‘14’], [‘7’, ‘10’], [‘7’, ‘1’], [‘7’, ‘20’], [‘8’, ‘20’], [‘8’,‘18’], [‘8’, ‘13’], [‘8’, ‘19’], [‘8’, ‘11’], [‘9’, ‘14’], [‘9’, ‘10’],[‘9’, ‘22’], [‘9’, ‘7’], [‘10’, ‘20’], [‘10’, ‘6’], [‘10’, ‘9’], [‘10’,‘14’], [‘10’, ‘1’], [‘11’, ‘12’], [‘11’, ‘18’], [‘11’, ‘19’], [‘11’,‘8’], [‘11’, ‘20’], [‘12’, ‘11’], [‘12’, ‘15’], [‘12’, ‘4’], [‘12’,‘18’], [‘13’, ‘2’], [‘13’, ‘8’], [‘13’, ‘1’], [‘13’, ‘20’], [‘14’,‘22’], [‘14’, ‘9’], [‘14’, ‘7’], [‘14’, ‘10’], [‘15’, ‘18’], [‘15’,‘12’], [‘15’, ‘21’], [‘15’, ‘5’], [‘17’, ‘4’], [‘17’, ‘21’], [‘17’,‘12’], [‘17’, ‘15’], [‘18’, ‘15’], [‘18’, ‘8’], [‘18’, ‘11’], [‘18’,‘3’], [‘19’, ‘2’], [‘19’, ‘11’], [‘19’, ‘8’], [‘19’, ‘15’], [‘19’,‘18’], [‘20’, ‘10’], [‘20’, ‘8’], [‘20’, ‘3’], [‘20’, ‘1’], [‘20’, ‘6’],[‘21’, ‘4’], [‘21’, ‘15’], [‘21’, ‘12’], [‘21’, ‘17’], [‘22’, ‘14’],[‘22’, ‘9’]]

FIG. 10 illustrates an exemplary diagram of max filtered force directedgraph drawing using force directed graph drawing library D3.js for Test19 according to an embodiment.

Table 13 lists max filtered (X,Y) coordinate output .json for Test 19.

TABLE 13 Max_Filtered_(X,Y) =[[“1”,25.739692374583022,110.77196657339223],[“2”,−5.82450970602064,400.86094598273485],[“3”,265.1754575498918,275.82090118723323],[“4”,595.170585626277,494.30095427604095],[“5”,420.0596610608523,292.61772700908426],[“6”,281.59031157551925,167.8606143136469],[“7”,68.03445255331252,−14.940592031033946],[“8”,124.66917364224715,374.90129830363986],[“9”,125.27074244409266,−136.20940086470318],[“10”,163.6924430565924,38.51434126129914],[“11”,222.54248350641112,484.0493657980021],[“12”,424.14622785293017,528.778755036457],[“13”,−17.51685619298813,273.82446335667703],[“14”,200.94074553375646,−97.3809900350623],[“15”,428.09897088569573,418.9177835977496],[“17”,558.0338430080125,557.0531965629032],[“18”,289.0916898014363,418.1648199359243],[“19”,132.9657836857719,498.1903546238373],[“20”,127.98961295505055,210.77125056388633],[“21”,559.2264333937758,416.6759429609442],[“22”,216.65673079234878,−220.9920115926178]]

Table 14 lists max filtered RSSI (X,Y) scaled values for Test 19.

TABLE 14 Node X Y 1 0.485199837 3.148275572 2 1 1 3 2.6128311372.58240768 4 5.522238973 1.866624961 5 3.759695045 2.857668212 62.454514487 3.393819676 7 0.466085118 4.152226939 8 1.8639674521.517831081 9 0.564807502 5.16260715 10 1.284265853 4.015145012 112.839969278 0.98941317 12 4.391088534 1.184713343 13 0.5926841391.87570907 14 1.203211165 5.078810467 15 4.139093686 1.977892381 175.417557435 1.324614369 18 3.146317356 1.628759538 19 2.237521420.660174098 20 1.469067059 2.696232341 21 5.068064549 2.328277131 22 1 6

Table 15 lists test layout actual location scaled for Test 19.

TABLE 15 Node X Y 1 1 3 2 1 1 3 3 3 4 5 1 5 4 3 6 3 4 7 1 4 8 2 2 9 2 510 2 4 11 3 1 12 4 1 13 1 2 14 1 5 15 4 2 17 6 1 18 3 2 19 2 1 20 2 3 215 2 22 1 6

Table 16 lists delta between RSSI D3js output and test layout location(scaled and actual) for Test 19.

TABLE 16 Scaled Distance Actual Distance Node (1 Unit = .5 Meters)(Meters) 1 0.535728339 0.267864169 2 0 0 3 0.56945858 0.28472929 41.011816371 0.505908185 5 0.279293411 0.139646705 6 0.8154808580.407740429 7 0.555191987 0.277595993 8 0.500990739 0.25049537 91.444374809 0.722187405 10 0.715894364 0.357947182 11 0.1603805260.080190263 12 0.432515041 0.21625752 13 0.425857307 0.212928653 140.217958407 0.108979203 15 0.140839627 0.070419813 17 0.6667936940.333396847 18 0.399034146 0.199517073 19 0.41460592 0.20730296 200.611689937 0.305844968 21 0.33525909 0.167629545 22 0 0

FIG. 11 illustrates a graph of max filtered RSSI scaled value accuracyfor Test 19. FIG. 6 is referenced for test layout nodes.

Example 20:—Test 20: Hanging Outside with Beacon in NE Corner

Overview:

Test 11 except measuring initial GPS, average GPS and test completedoutside with wifi and Bluetooth devices running at large distance fromtest (20 yards).

Procedure:

-   -   1: Elevate phones 5.5 feet off the ground using the hanging        structure described in Precursor to Experiments Procedure step 2        in the pattern demonstrated in FIG. 6. Place the hanging        apparatus outside. Additionally, note that in FIG. 6, the        lighter grid lines indicate 0.25 meter intervals while the        thicker grid lines are 0.5 meters. Orient the grid so the beacon        is in the North-East most corner of it.    -   2: Turn the phones and wait until phone pickup signal.    -   3: Measure average GPS over 5 minutes with GPS tracking android        app with no devices or people within 20 yards of the beacons and        receivers.    -   4: Log Data as shown in FIG. 12.    -   5: Convert GPS Angular decimal form data into an (x,y) scatter        plot using an equirectangular projection as shown below:        -   x≤rλcos(φ₀)        -   y=rφ        -   φ₀ denotes a latitude close to he center of your map        -   r is the radius of the earth        -   λ is decimal form latitude        -   φ is decimal form longitude    -   6: Scale the GPS (X,Y) scatterplot as shown in max filtered        pseudocode part IV to create a comparative location graph as        shown in Table 18 and FIG. 12.

Conclusion:

Based upon the way we completed our testing, GPS has a 15% sub-meteraccuracy compared to our method of location and no sequencing accuracy.Also nodes 15 and 11 as well as nodes 6 and 7 had identical locations,which is incorrect. Finally phones 1 and 16 in the test were unable toreceive GPS signal in our location.

Table 17 lists the GPS 5 minute average predicted location for Test 20.

TABLE 17 Node X Y 11 43.18116 −111.02999 10 43.18118 −111.02997 1343.18115 −111.02996 12 43.18115 −111.03001 15 43.18116 −111.02999 2143.18114 −111.03 17 43.18119 −111.02999 19 43.18114 −111.02998 1843.18116 −111.03 22 43.18114 −111.02997 20 43.18117 −111.02998 343.18113 −111.02997 2 43.18112 −111.02998 5 43.18114 −111.02999 443.18115 −111.03 7 43.18115 −111.02998 6 43.18115 −111.02998 9 43.18117−111.02995 8 43.18122 −111.02996 14 43.18116 −111.02997

Table 18 lists the GPS (X,Y) scaled predicted location values for Test20.

TABLE 18 Node X Y 11 43.18116 −111.02999 10 43.18118 −111.02997 1343.18115 −111.02996 12 43.18115 −111.03001 15 43.18116 −111.02999 2143.18114 −111.03 17 43.18119 −111.02999 19 43.18114 −111.02998 1843.18116 −111.03 22 43.18114 −111.02997 20 43.18117 −111.02998 343.18113 −111.02997 2 43.18112 −111.02998 5 43.18114 −111.02999 443.18115 −111.03 7 43.18115 −111.02998 6 43.18115 −111.02998 9 43.18117−111.02995 8 43.18122 −111.02996 14 43.18116 −111.02997

Table 19 lists the GPS node test layout actual location for Test 20.

TABLE 19 Node X Y 1 1 3 2 1 1 3 3 3 4 5 1 5 4 3 6 3 4 7 1 4 8 2 2 9 2 510 2 4 11 3 1 12 4 1 13 1 2 14 1 5 15 4 2 17 6 1 18 3 2 19 2 1 20 2 3 215 2 22 1 6

Table 20 lists the GPS delta between recorded and actual location(scaled and actual) for Test 20.

TABLE 20 Scaled Distance Actual Distance Node (1 Unit = .5 Meters)(Meters) 2 0 0 3 1.578117079 0.789058539 4 12.38899562 6.194497808 57.749257355 3.874628678 6 5.885212601 2.9426063 7 4.0521209342.026060467 8 20.55923491 10.27961746 9 9.35300469 4.676502345 1010.33495082 5.167475409 11 10.33908354 5.169541768 12 13.662976996.831488495 13 7.446026537 3.723013268 14 4.907490352 2.453745176 1510.83175276 5.415876381 17 18.49628141 9.248140705 18 11.684534795.842267397 19 4.714537933 2.357268966 20 9.323169888 4.661584944 2111.16305481 5.581527405 22 0 0

FIG. 12 illustrates a graph for comparison with GPS accuracy for Test20.

Based on Examples 1-20, it was found that utilizing the system describedherein increased in accuracy, and consistency, as the actual distancebetween devices decreased. Therefore, given the crowd-like plurality ofdevices in most desirable applications of localization, includingfestivals, households, malls, offices, etc, we can organize the devicesaccording to the strongest RSSI connections, or the devices that aremost likely to be adjacent to each other. If a data set of mobiledevices with their strongest RSSI connections are used forces can beattached to those connections, limit the number we employ, and render agrid using graph theory. This allows us to take the knowledge of whichdevices are nearest to each other and stitch the order of the devicestogether in a cohesive grid. Using this method, described at a highlevel here and in greater detail later, we were able to achieve thecreation of a grid comprising of relative device-to-device position towithin sub-meter accuracy.

In one embodiment, the system is configured to localize each node of asystem relative to every other node in the system. A node may include amobile device with sensor, e.g., Bluetooth Low Energy (BLE) sensor. Thesensor can act both as a BLE beacon and receiver as shown FIG. 1. BLERSSI values between nodes are used to find proximities between nodes.Proximities between nodes data is run through a Force Directed GraphDrawing algorithm. In one embodiment, the system uses mobile stations,e.g., Android moto E 2nd gen, as BLE capable nodes. One or more of thesedevices were used as nodes, in a preferred embodiment, 10 or more mobiledevices are used as nodes, in a more preferred embodiment, twenty ormore these mobile stations can be used, with BLE capabilities to recordBLE RSSI data in a half meter grid (a right triangle with two equalsides of 6 mobile devices).

In one embodiment, raw Bluetooth low energy Received Signal StrengthIndication data from phones was measured and/or recorded. A hash tablethat accumulates and records the connection, direction and RSSI value ofeach beacon phone to receiver phone connection, is shown in Table 7.Every connection has multiple RSSI values; therefore, we filter all RSSIvalues through Kalman filter and take the average of those filteredvalues in both directions to determine a RSSI value that will be used inour system as shown in FIG. 7 and Table 8.

We then sort the RSSI value of each phone's connections from greatest toleast. We then iterate over every phone (for example: Phone 1) andchooses the highest 6 connections where that phone was a receiver beacon(for example: [Phone 20, Phone 13, Phone 10, Phone 7, Phone 14, Phone11]→Phone 1).

We then find the standard deviation of those 6 connections’ values. Ifstandard deviation value is higher than 3, we pass the two highestvalues to the next phase of the system; if standard deviation value ishigher than 2 but less than or equal to 3, we pass the three highestvalues to the next phase of the system; if standard deviation value islower than or equal to 2, we pass the four highest values to the nextphase of the system.

We then collect all the values (highest connections) passed by theprevious stage of the program. This collected data (Table 9) isconnections/edges data that will be passed into part III of thepsuedocode included below. Extract points/nodes data that will be passedinto part III by getting all the unique points of the connection/edgedata.

Pass data from part II into Force Directed Graph Layout in part III.

Create a two dimensional coordinate plane where our graph will besimulated.

A simulation runs in 2D plane and reaches equilibrium, a state wherenodes stop moving.

Record (X,Y) data of every point on the 2D plane.

Scale the 2D Plane based upon 2 known mobile station locations. AveragedKalman Filter Pseudocode:

//Part II pseudocode A = createTable( ) for every phone in phones:   B =createTable( )   for every connection in phone:     if connection doesnot exists:       create a new key and add RSSI value as the nextelement       of key     else:       add RSSI value to previous createdkey   for every b in B:     kalmanFilter(b)     average(b)   A.add(B) C= getUniqueReceivers(A) D = createTable( ) E = createTable( ) for everyc in C:   D.add(c)   D.sort( )   D = D[0:6]   sd = standardDeviation(D)  if sd > 3:     D = D[0:2]   else if sd > 2:     D = D[0:4]   else ifsd <= 2:     D = D[0:5]   E.add(D)   D.makeEmpty( ) // E isconnections/edges data F = uniquePoints(E) // F is points/nodes data//Part III pseudocode G = created2DPlane( )G.simulateForceDirected(nodes = F, edges = E)   .linkDistance(30)  .charge(−10000)   .gravity(0.45) if G.simulationEnded:   for f in F:    f.log(x,y)

In one embodiment, raw Bluetooth low energy Received Signal StrengthIndication data from phones was measured and/or recorded. A pythoncreates a hash table that accumulates and records the connection,direction and RSSI of each beacon phone to receiver phone connection asshown in Table 7. Every connection has multiple RSSI values; therefore,we filter all RSSI values to find the maximum RSSI value and takeaverage of those max values to determine a RSSI value that will be usedin our system as shown in FIG. 9 and Table 11.

Sort the RSSI value of each beacon phone connections from greatest toleast, iterate over every phone (for example: Phone 1) and choose thehighest 6 connections where that phone was a receiver beacon (forexample: [Phone 20, Phone 13, Phone 10, Phone 7, Phone 8, Phone 6]→Phone1).

Find standard deviation of those 6 connections' values. If standarddeviation value is higher than 3, we pass the two highest values to thenext phase of the system; if standard deviation value is higher than 2but less than or equal to 3, we pass the three highest values to thenext phase of the system; if standard deviation value is lower than orequal to 2, we pass the four highest values to the next phase of thesystem.

Collect all the values (highest connections) passed by the previousstage of the program. This collected data is connections/edges data(FIG. 21) that will be passed into part III. Extract points/nodes datathat will be passed into part III by getting all the unique points ofthe connection/edge data.

Pass data from part II into D3.js Force Directed Graph Layout(https://d3js.org/) in part III.

Create a two dimensional coordinate plane where our graph will besimulated.

A simulation runs in 2D plane and reaches equilibrium, a state wherenodes stop moving.

Record (X,Y) data of every point on the 2D plane.

Scale the 2D Plane based upon 2 known mobile station locations.

Max Filtered Pseudocode:

//Part II pseudocode A = createTable( ) for every phone in phones:   B =createTable( )   for every connection in phone:     if connection doesnot exists:       create a new key and add RSSI value as the nextelement       of key     else:       add RSSI value to previous createdkey   for every b in B:     Max(b)     average(b)   A.add(B) C =getUniqueReceivers(A) D = createTable( ) E = createTable( ) for every cin C:   D.add(c)   D.sort( )   D = D[0:6]   sd = standardDeviation(D)  if sd > 3:     D = D[0:2]   else if sd > 2:     D = D[0:4]   else ifsd <= 2:     D = D[0:5]   E.add(D)   D.makeEmpty( ) // E isconnections/edges data F = uniquePoints(E) // F is points/nodes data//Part III Pseudocode (this is a Preferred Embodiment)(Output ShownTable 13)

G = created2DPlane( ) G.simulateForceDirected(nodes = F, edges = E)     .linkDistance(30)      .charge(−10000)      .gravity(0.45) ifG.simulationEnded:   for f in F:     f.log(x,y) //Part IV f = D3.js(x,y) values B = Subtract Node 2 X values from all other Nodes X valuesand subtract Node 2 Y value from all other Nodes Y values. Set Node 2equal to (0,0) C = Graph B rotated so that Node 22 lies on Y axis and Xvalue of Node 22 is equal to 0 D = C with graph translated so that Node2 is positioned at (1,1) E = D scaled so distance between Node 2 andNode 22 is equal to 5

Embodiments of the invention allow BLE capable nodes (mobile devices,beacons, etc) to know their relative position to other BLE capable nodes(mobile devices, beacons, etc). This relative positioning can beemployed with other resources to achieve absolute position, which maycomprise:

-   -   a. Access points where a BLE node is hardcoded with absolute        location;    -   b. GPS positioning to create an access point or awareness of        surrounding environment;    -   c. Adaptive recognition of environments using an awareness of        the change in relative positions, anomalies, and over-time        logging of presumed constraints (walls, thresholds, tables,        etc); and/or    -   d. Relative position localization without use of any external        hardware (beacons).

In an embodiment, we are employing APs, or access points in order to seta known distance between two mobile stations to scale the force directeddrawing mesh which allows for submeter phone location accuracy. We arenot required to survey areas to learn their RFID characteristics (asopposed to the methods (e.g., trilateration and other methods) in therelated art).

Unlike other techniques that usually use trilateration and/ortriangulation in the related art, embodiments of the invention may useForce Directed Graph Drawing, which is generally used for visualizationpurposes.

FIG. 13 illustrates an exemplary flow diagram of a wireless locationmethod according to an embodiment.

In an embodiment, wireless location method 1300 may be performed by asystem of one or more nodes (e.g., mobile devices). The nodes of thesystem may be pre-arranged or ad-hoc (e.g., nodes may join or leave thesystem at times), and the processing of system may occur at the nodes ora server (centralized or distributed) or shared between somecombinations of the nodes and server. In an embodiment, the nodes may beconfined to a relatively small geographic location (e.g., a room wherethe nodes are generally sub-meters apart).

The wireless location method 1300 starts by collecting, for each node, aRSSI value (or a value indicative of signal strength) of each of theother nodes over a time interval 1310. In an embodiment, each node maybe pre-configured to receive wireless signals from other nodes (e.g.,passively or actively receiving signal strengths of the other nodes in awireless channel (e.g., Wi-Fi, Bluetooth, etc.)) or may be configured toreceive such wireless signals. The RSSI values are collected over a timeinterval (e.g., 5 seconds, 30 seconds, 1 minute, 2 minutes, etc.). In anembodiment, at least some of the nodes may be part of a mesh (e.g., aBluetooth mesh or an ad-hoc Wi-Fi mesh) and the RSSI values may becollected from signals of other nodes of the mesh.

It is noted the node may receive one or more RSSI values at differenttimes within the time interval (e.g., one of the other nodes maytransmit a wireless signal to the receiving node at every second of afive second interval). In an embodiment, the RSSI values of wirelesssignals from a particular node may be aggregated to one RSSI valuerepresentative of the particular node over the time interval. Forexample, the maximum RSSI value from the particular node for the timeinterval may be used as the RSSI value for the particular node. Inanother example, the average or some weighted aggregation of the one ormore RSSI values may be used as the RSSI value for the particular node.

In an embodiment, all of collected RSSI values from each node to atleast some of the other nodes may be entered as a matrix or some otherdata representation for processing at a server and/or one or more of thenodes.

In an embodiment, a node may only receive RSSI values from a subset ofthe nodes (e.g., when a node is not in wireless communication range ofanother node, e.g., when the room or geographic location may much largerin size than the wireless communication range).

The wireless location method 1300 processes a normalization of thecollected RSSI value 1320.

For a mobile device, it is noted that the RSSI values may generally bemeasured as a power ratio (in decibels) of the measured power withreference to one milliwatt (dBm). Typical RSSI values for mobile devicesdepend on the wireless signal type, wireless conditions, and the poweroutput of the mobile devices. For example, a typical signal for nearbydevices may range >−40 dBm to −80 dBm, and relatively poor signals maybe <−100 dBm to <−120 dBm.

It is noted, at least with same or similar types of equipments (e.g.,mobile device make and model, radio chipset, antenna, etc.), that theRSSI values received by a receiving node from a transmitting would behigher when the two nodes are closer to one another. However, when theequipments differ, the mixture of RSSI values may be represent or hintat comparable physical distances. For example, a RSSI value for awireless signal transmitted by a node of one type equipment may differfrom a wireless signal transmitted by another node from a same physicaldistance (or even the same position) due to the different equipment(e.g., affecting the power level output of the wireless signal).

In an embodiment, a goal of normalization is to normalize acorrespondence of the received RSSI value to a physical distance (whichcan be relative distances among a number of nodes or the absolutephysical distances between the nodes) between the receiving node and thetransmitting node (e.g., removing or minimizing the equipment and/orother factors). In an embodiment, the range of RSSI values may benormalized to a standard relative range (e.g., relative distances ornormalization values between zero and one).

In an embodiment, the normalization may set an upper bound and a lowerbound for the RSSI values to be normalized to a range between zero andone. In one example (which will be further discussed in the examplebelow), the upper bound may be set to an RSSI value of −40 dBm and alower bound set to an RSSI value of −72 dBm. Therefore, RSSI values thatare above −40 dBm may be normalized to 1, RSSI values that are below −72dBm may be normalized to 0. In an embodiment, the upper bound and thelower bound may correspond to physical distance extremes between thereceiving node and the transmitting node where it may be less likelythat a change in the RSSI value represents a meaningful change in thephysical distances. With the previous example, an RSSI value that isnear −40 dBm may correspond to a high likelihood that the receiving nodeand the transmitting node are very close to one another, and an RSSIvalue that is below −72 dBm may correspond to a high likelihood that thereceiving node and the transmitting node are already at a fringedistance of communication and/or detectability.

In an embodiment, the normalization may be performed according to afunction, which may be linear, exponential, or other types of functions.For example, normalization using a linear function may map the boundeddomain of RSSI values to a range of between zero and one in aproportional relationship. In another example, normalization using anexponential function may map the RSSI values to between zero and one ina same direction but non-proportional relationship. In an embodiment,the normalization function may correspond to a fit (e.g., regressionfit) of the RSSI values (e.g., a fit of a scatter plot of the RSSIvalues). The fit may be performed automatically, manually, or by acombination of automatic and manual fit.

In an embodiment, a group of RSSI values from the same and/or relatedequipments (e.g., mobile devices by the same manufacturer, mobilesdevices using the same or related types of radio chipset, mobile devicesusing the same or related operating systems, mobile device antenna,etc.) may be normalized using the same normalization function, while adifferent group of RSSI values from the same and/or related equipmentsmay be normalized using a different one normalization function. Forexample, it may be identified, detected, and/or discovered that certainRSSI values were transmitted from a same type of equipment (e.g., AppleiPhones with the same model number, radio chipset, and antenna). SuchRSSI values may be normalized by the same normalization function whileother devices may be normalized using a different normalization function(e.g., based on the other devices' equipment types). In an embodiment,devices with unknown identifications may be grouped and normalized usinga generic normalization function.

In an embodiment, all devices (regardless of equipment type) may benormalized using the same generic normalization function. For example,it may be anticipated (or generally statistically likely) that the crowdof mobile devices used in some application may contain a mixture ofvarious equipment types. A generic normalization function that workswith different various equipment types may at least decrease generalRSSI value differences that may be attributed to different equipmenttypes (e.g., normalize RSSI values towards an average equipment type).This may be advantageous to decrease the number of equipment typeanalysis, normalization processing, and/or other processing. Further,using one normalization function may help improve the consistency of thenormalized RSSI values (e.g., there may not be enough equipment of onetype to fit a normalization function for that specific type). In anembodiment, the generic normalization function may be pre-determined orfitted as needed during the performance of an application.

In an embodiment, one or more normalization functions may bepre-determined from tests prior to a performance of an application. Forexample, tests may be performed to determine for a room with one or acombination of various equipment types typical for the application (orthe actual room for the application). In an embodiment, tests may beperformed in a generic room for various applications (or to be usedgenerically when the room for the application is unknown. In anembodiment, the tests may be performed with various time intervals (of1310) to obtain the normalization functions.

In an embodiment, the RSSI values may be analyzed and/or sorted toremove certain outliers prior to normalization, which may provide moreprecise and consistent dataset of RSSI values or for other purposes.

In another embodiment, the RSSI values may not need to be normalized,and the raw RSSI value may be used as inputs for the location coordinateevaluations in the wireless location method 1300.

The wireless location method 1300 performs location coordinateevaluation of nodes using the normalized RSSI values 1330.

In an embodiment, the location coordinate evaluation may be performed byvarious machine learning techniques as known now or may be later derivedon the normalized RSSI values. For example, the location coordinateevaluation may be performed by a self-organizing map (SOM) on thenormalized RSSI values (e.g., in a matrix form). The SOM takes inparameters of the normalized RSSI values, learning rate, and sigma.Other techniques such as manifold learning (e.g., MDS) and artificialneural networks (e.g., neural gas) may be used.

In an embodiment, the SOM may use pre-determined parameters (e.g.,learning rate and/or sigma) as inputs that were pre-determined to havelow error rates. For example, tests may be performed in a room with anumber of nodes (e.g., mobile devices) in known positions. Variousranges of parameters (e.g., learning rate and/or sigma) may be used asinput to the SOM with the normalized RSSI values. The output of the SOMmay then be compared with the known positions of the nodes to determinethe parameters that would yield the lowest error rate. Thepre-determined parameters may be used as inputs for other locationcoordinate evaluations (e.g., with other nodes) at unknown positions.

In an embodiment, the SOM may be a residual sum of squares measurementas the error (e.g., based on the coordinates of the nodes at theiractual coordinates). It is noted that an error measurement based on theactual coordinates may include a weight towards errors of nodes that arefurther apart (e.g., when the greater error of nodes further apart areamplified compared with nodes closer apart). In an embodiment, asequence error may be used (e.g., one that considers error in theorientation (e.g., relative position) but less on the spacing (e.g.,actual coordinates)), which may be preferable for applications thatconsiders the orientation or relative positions of the nodes moreimportant.

In another embodiment, a variable error may be used (e.g., one thatconsiders a clustering of the evaluated nodes and may pull nodes furtherapart if clustering is large). For example, the space of the evaluatedcoordinates may be separated into individual boxes covering a certainarea of the space. If the evaluated node cluster (e.g., having more thanone node in a space), the error may increase depending on the degree ofclustering (e.g., number of nodes in an area). This error evaluation maybe helpful for testing or other applications where the nodes have aknown or expected degree of clustering or non-clustering.

In an embodiment, tests may be performed in a generic room for variousapplications (or to be used generically when the room for theapplication is unknown).

In other embodiments, the location coordinate evaluation may use othermachine learning techniques or may use non-machine learning techniques(e.g., regression analysis, force directed graph drawings as discussedabove, or other techniques).

The wireless location method 1300 orients the evaluated locationcoordinates 1340. It is noted that in some location coordinateevaluation (e.g., SOM), the correct orientation of the crowd of nodes isnot evaluated. In an embodiment, the location coordinates may beoriented using singular value decomposition or other techniques as knownnow or may be later derived. In an embodiment, the correct orientationof the location coordinate may not be needed (e.g., in an applicationwhere only the relative positions among the nodes would want to beascertained).

In an embodiment, the orientation may be performed and/or assisted byGPS and/or other wireless location techniques. For example, GPS may beused to estimate a less precise (but with absolute coordinates) positionof one or more nodes (including nodes that are farther apart).Therefore, the orientation between some of the nodes may be estimates(e.g., using the absolute coordinates) using the GPS (or other wirelesslocation techniques) and a more precise orientation (using the evaluatedlocation coordinates) may be based on the estimated orientation.

In an embodiment, the orientation may be performed with respect to oneof the nodes (e.g., a lead node).

The wireless location method 1300 performs the application using theoriented location coordinates of the nodes 1350.

In an embodiment, the oriented location coordinates of the nodes (e.g.,of mobile devices) may be used to arrange a coordinated display (e.g.,arranging a lightshow using the mobile devices at their location). Forexample, each mobile device contains a software (e.g., an app) which cancontrol the display of the mobile device. The software may be incommunication with a server that has access to the oriented locationcoordinates of the mobile devices. The server may use the orientedlocation coordinates to have the relevant mobile device at a location tochange to a certain display, such that the individual displays of themobile devices may make up a larger display over the area (e.g., theroom or the crowd). For example, the displays in conjunction may becontrolled to display certain patterns of color, as pixels of largeimages, or other displays. In an embodiment, the displays may changeover time to portray changing images, changing colors, animation, orother motions in the displays. In an embodiment, a sync time (e.g.,communication time between the mobile devices and the server or othercommunication lag events) may need to be accounted for (e.g., when thedisplay is fast-changing and would need to be synced with lowertolerance to prevent perceptible lags). For example, a uniform lag time(or buffer) may be used to allow for the communication lag so that allof the displays sync to the common lag time. In an embodiment, theserver may be in communication with a controller (e.g., a DJ station)which may automatically or manually issue control to the individualmobile devices (e.g., having the lightshow sync to a song that is beingplayed or will be played). In an embodiment, the data transfer andcomputation (e.g., at least a portion performed by the serve) may beperformed by and/or within a mesh of at least some of the nodes (e.g., aBluetooth mesh).

In an embodiment, locations from GPS or other wireless location systemmay assist and/or supplement with the wireless location method. Forexample, when a node is out of wireless communication range (forobtaining the RSSI values) with a subset of nodes, GPS (having anaccuracy of 5 m) or other location wireless system may be used inconjunction with RSSI values data for all of the nodes (e.g., anothernode that is in range with both the node and at least a subset of thesubset of nodes) to position the node. In an embodiment, the wirelesslocation method may work with sub-meter range nodes (e.g., 0.2 m rangesfor a room packed with human operators carring nodes) and also nodes inlarger rooms through the combination of the wireless location method,GPS, and/or other location system (e.g., 100 m (range of Bluetooth) orlarger).

It is noted that certain “location” problems may be solved by video,e.g., things like a robot reconnecting server chords, a surgical bottying sutures, or a tractor trailer harvesting wheat. Embodiments may becomplementary to these solutions, in organizing identities (e.g., who iswho where?).

In other embodiments, the wireless location method as disclosed hereinmay be applicable to other applications, including:

Tracking agricultural machine movements (e.g., tracking positions allyour tractors):

In one embodiment, tractors, semi-trailers, drones, small task bots, orother modernized farm equipment may be coordinated to farm with greaterautonomy. An advantage is the accurate positioning of farm devices evento very small granularity, e.g. knowing semi-trailer and tractor pairsin the field, accountability of farmhand to his/her equipment, etc. Itis noted that such resolution of position is beyond the capabilities ofGPS in a case where several devices are near each other, e.g. in a barn,equipment staging for the day, in a warehouse, or some cases of closelycoordinated field work. In an embodiment, machines movement may bedirected in agricultural environments where GPS is inaccessible and/orinadequate, e.g. wine cellars, warehouses, underground storage, urbanfarms, etc.

Tracking underground machine movements (e.g., mining machines, trackingpositions of drills that are moving underground, etc.):

An embodiment may apply to autonomous, underground machine movements.GPS cannot penetrate the earth and therefore underground environmentssuch as mines, quarries, storage facilities, and others require somesystem for coordination and localization. In an embodiment, locationservices can be provided without the need for a reference frame as thedevices themselves are the reference points.

Tracking industrial vehicle movements:

In one embodiment, machines and devices on a worksite can be organizedby location without input from workers. An advantage is the granularityof accuracy allowing for accurate knowledge of accountability,culpability, and movement of devices and their relation to humans. Forexample, if a worker improperly attaches a trailer to his/her truck,such data may be immediately detected and be available to management. Inanother use case, if specific equipment is required to move from oneside of the worksite to the other without human input, the equipment canbe called upon and make delivery of itself in any environment includingunderground, warehouses, hulls of ships, docks, storage facilities,quarries, mines, underwater, etc. Industrial vehicles include drones,submarines, tractors, trucks, robots, semi-trailers, trailers,forklifts, diggers, dredgers, tug boats, winches, cranes, crane cabs,sky lifts, drones, backhoes, excavators, and any other machine or devicethat may be found on a worksite of any scale.

Tracking locations of animals and fishes (e.g., attach a module to theanimals and research their movements, their communication with oneanother):

An embodiment can be used to count, localize, signal, and direct herdanimals or fish. For example, it could be used to herd sheep or cattlewhen attached to a vibrating/shock collar.

Tracking and finding items (e.g., Pixie (https://getpixie.com/))

In one embodiment, a plurality of devices can be used to locate aspecific device within the crowd.

Tracking planes at airports:

An embodiment can be used to organize airplanes by identifier on airportramps for the purpose of alerting ATC and Ground Control to the identityand location of each plane passively and without visual recognition,which may be particularly useful in congested airports and/or lowvisibility operations.

Tracking Shoppers location in a store:

An embodiment can be used to database the location of persons in anystore including their identity.

Tracking movement of current in river via multiple sensors movement:

In an embodiment, the location of the sensors may be organized.

Self organized and location based reporting of agricultural fieldsensors:

In one embodiment, any sensor network could self-organize, which maynegate the need for an IT professional to specifically coordinatesensors or establish specific connections between sensors. For example,a lighting team could simply install at random enabled lights in a roomand the disclosed art would record the position of all sensors providinglocation based functionality and control. Agriculture is increasinglydeploying field sensors to monitor the specific needs of crops toincreasingly small resolution, deploying these sensors enabled with theembodiment would be extremely simple compared with the detailed locationsetup required today. Self-organizing sensors as made possible by thepresent disclosure also apply to car parking lots, warehouse storagesystems, freight cargo box organization/coordination, managing accesspoints in buildings of any size, etc.

Self-driving car identity based communications (letting one carcommunicate with the car adjacent, or in front and adjacent withoutrelying on visuals):

In an embodiment, self-driving cars may be made aware of theidentification of surrounding self-driving cars. An exemplaryapplication is the settlement of insurance claims, e.g. should two carsdamage each other but continue driving, both cars are immediately madeaware of the other car's information. It may also be used to establish anetwork for the purpose of casual chat, information transfer, and otheridentity and location based network services.

Crowd control at events:

An embodiment may be used at events for crowd control, interaction, andmonitoring purposes. For example, treating smart phones as nodes thephones can be networked to direct individuals toward their interest withthe traffic interest of the crowd at large in consideration.Inefficiencies in crowd movement can be spotted and mitigated.Furthermore, ticketed events could be managed without walls or fences byway of identity based policing. If a person is not recognizable to thesystem or not appearing by way of location where they ought to beappearing, than managers can remove the person or deny services.

Interacting with virtual reality based on where you are and who you arewith:

In an embodiment, UI components or digital assets can be presented to auser in response to their surrounding environment, e.g. an adjacent usermay have an avatar, a specific venue may have a food and drink orderingsystem that changes with location.

In venue or event food, merchandise, and/or drink delivery:

In one embodiment, any good or service requiring delivery at an outdoorsor indoors event or venue or locale can be accurately routed using theart.

Store inventory protection:

In one embodiment, the disclosed art can be used to alert store ownersto the unwanted disappearance of an item.

In embodiments, the wireless location method as disclosed herein may beapplicable to other applications, including:

-   -   Self-driving car identity based communications (letting one car        communicate with the car adjacent, or in front and adjacent        without relying on visuals)    -   Self organized sensors for IT professionals managing access        points in large buildings    -   Self organizing storage warehouse    -   Car lot map creation    -   Crowd movement data    -   Allow for fenceless venues or events (but still controlled        attendance)    -   Replace ID cards for location entry, emergency services, and        data collection    -   ATC location organization without radar (flights over the ocean,        planes already equipped with their own radar—only valuable to        close flying planes or if satellites are not an option        (uncharacteristically dense and high clouds)    -   Underwater fleet management    -   Line weighting applications (if a line forms at a place, alert        authority to resolve bottleneck)        -   Self driving car traffic, concessions or rides, venue            bathrooms, etc.    -   Emergency routing for victims and action personnel    -   Automatically establishing a line for bot systems based on        identity    -   Calculate an efficient traffic movements (e.g., based on        “Bernoulli's principle”)    -   Book organization and finding system (e.g., at a library)    -   Presenting UI based on where you are and what is around you    -   Satellite orientation and organization    -   Tracking people traveling, e.g., in remote places with or        without cellular connections (like mountain hiking, extremely        remote locations such as deserts, rainforests, North or South        poles), where the travelers can keep track of one another    -   Tracking self-driving vehicles location on the road (e.g.,        solving an identity problem whereas video alone may solve the        driving problem)

In another embodiment, the wireless location methods as disclosed hereinmay be used for other applications that may be handled by GPS or otherwireless location methods. It is noted that GPS hardware and chips areusually much more costly than most other radio modules (e.g., that hasmeasurable RSSI) like BLE hardware and chips.

EXAMPLES

Without intending to limit the scope of the invention, the followingexamples illustrate how various embodiments of the invention may be madeand/or used.

Various tests were performed using various mobile phones arranged inknown positions within a space. The mobile phones were configured totransmit wireless signals, and other mobile phones within the space wereconfigured to receive the wireless signals and determine thecorresponding RSSI values within a time interval. The collected RSSIvalues were aggregated and normalized according to an exponential fit ofthe RSSI value data, after outliers were removed. The normalized RSSIvalues were passed to a SOM for unsupervised machine learning usingvarious parameters for the learning rate and sigma value, resulting inevaluated location coordinates based on the normalized RSSI values. Theevaluated location coordinates were oriented to an orientationcomparable with the original known positions using singular valuedecomposition. The oriented location coordinates (for each of the testconfigurations and SOM parameters) were compared with the original knownpositions to determine an error to the original known positions for thegiven test configurations and SOM parameters.

Table 21 lists the mobile devices used in the tests.

TABLE 21 RADIO (WIFI Phone # SOFTWARE HARDWARE or 4G LTE) 1 AndriodSprint Moto E (2nd Gen) with 4G LTE XT1526 WIFI 2 Andriod Sprint Moto E(2nd Gen) with 4G LTE XT1527 WIFI 4 Andriod Sprint Moto E (2nd Gen) with4G LTE XT1528 WIFI 5 Andriod Sprint Moto E (2nd Gen) with 4G LTE XT1529WIFI 6 Andriod Sprint Moto E (2nd Gen) with 4G LTE XT1530 WIFI 7 AndriodSprint Moto E (2nd Gen) with 4G LTE XT1531 WIFI 8 Andriod Sprint Moto E(2nd Gen) with 4G LTE XT1532 WIFI 9 Andriod Sprint Moto E (2nd Gen) with4G LTE XT1533 WIFI 10 Andriod Sprint Moto E (2nd Gen) with 4G LTE XT1534WIFI 11 Andriod Sprint Moto E (2nd Gen) with 4G LTE XT1535 WIFI 12Andriod Sprint Moto E (2nd Gen) with 4G LTE XT1536 WIFI 13 AndriodSprint Moto E (2nd Gen) with 4G LTE XT1537 WIFI 14 Andriod Sprint Moto E(2nd Gen) with 4G LTE XT1538 WIFI 15 Andriod Sprint Moto E (2nd Gen)with 4G LTE XT1539 WIFI 17 Andriod Sprint Moto E (2nd Gen) with 4G LTEXT1540 WIFI 18 Andriod Sprint Moto E (2nd Gen) with 4G LTE XT1541 WIFI20 Andriod Sprint Moto E (2nd Gen) with 4G LTE XT1542 WIFI 21 AndriodSprint Moto E (2nd Gen) with 4G LTE XT1543 WIFI 22 Andriod Sprint Moto E(2nd Gen) with 4G LTE XT1544 WIFI 23 IOS Iphone 7 With Defender OtterBox 4G LTE (MNAU2LL/A) 24 Andriod Galaxy S6 (SAMSUNG-SM-G920A) WIFI 25IOS Iphone 6s With Thin Plastic Case 4G LTE (MKQA2LL/A) 26 AndriodGalaxy S7 (SAMSUNG-SM-G930A) WIFI 27 IOS Iphone 5 With Defender OtterBox WIFI (MD654LL/A) 28 Andriod Galaxy S5 (SAMSUNG-SM-G900A) WIFI 29 IOSIphone 6 (MG5W2LL/A) WIFI 30 Andriod Galaxy S5 (SAMSUNG-SM-G900A) WIFI31 IOS Iphone 5S (ME307LL/A) WIFI 32 Andriod Galaxy S6(SAMSUNG-SM-G920A) WIFI 33 IOS Iphone 5 (MD638LL/A) WIFI

FIG. 14A illustrates an arrangement of nodes for Test A according to anembodiment.

For Test A, the mobile devices (as listed in Table 21) are arranged 0.5m (˜19.685 inch) by 12 inch apart (e.g., close cluster), at a uniformheight, with similar devices close to each other. It is noted thatmobile device 30 only received RSSI values (and did not transmit), andmobile device 5 was malfunctioning and gave false readings.

FIG. 14B illustrates an arrangement of nodes for Test B according to anembodiment.

For Test B, the mobile devices (as listed in Table 21) are arranged 0.5m (˜19.685 inch) by 24 to 25.5 inch apart (e.g., far cluster), at auniform height, with similar devices close to each other. It is notedthat mobile device 30 only received RSSI values (and did not transmit),and mobile device 5 was malfunctioning and gave false readings.

FIG. 14C illustrates an arrangement of nodes for Test C according to anembodiment.

For Test C, the mobile devices (as listed in Table 21) are arranged 0.5m (˜19.685 inch) by 24 to 25.5 inch apart (e.g., far cluster), at auniform height, with devices of different equipments intermixed in theirarrangement. It is noted that mobile device 30 only received RSSI values(and did not transmit), and mobile device 5 was malfunctioning and gavefalse readings.

FIG. 14D illustrates an arrangement of nodes for Test D according to anembodiment.

For Test D, the mobile devices (as listed in Table 21) are arranged 0.5m (˜19.685 inch) by 12 inch apart (e.g., close cluster), at a uniformheight with devices of different equipments intermixed in theirarrangement. It is noted that mobile device 30 only received RSSI values(and did not transmit), and mobile device 5 was malfunctioning and gavefalse readings.

FIG. 15A illustrates a graph showing measured relationship between RSSIvalue and distance for Tests A-D with 5 sec RSSI reading interval.

With the equipments listed in Table 21 under Tests A, the received RSSIvalue (at each equipment) from the transmitting equipments are measuredand plotted in FIG. 15A with respect to their known distances to thetransmitting equipment. The distances were normalized to be between zeroand one.

It is noted that the equipments (in Table 21) can be grouped by theequipment type (old Android devices, new Android devices, and Iphones),as shown in FIG. 15A.

FIG. 15B illustrates a graph showing measured relationship between RSSIvalue and distance with an exponential fit of the graph for Tests A-Dwith 5 sec RSSI reading interval.

An exponential fit may be used for the measured relationship betweenRSSI value and distance for Tests A-D. As discussed with respect to thewireless location method 1300, the fit may be used to normalize otherRSSI value measurements in future instances (e.g., when distance isunknown and wants to be determined).

FIG. 15C illustrates a graph showing measured relationship between RSSIvalue and distance with outliers removed for Tests A-D with 5 sec RSSIreading interval.

It is noted that with outliers removed, a fit may be closer to the plot(e.g., has less error).

FIG. 15D illustrates a graph showing measured relationship between RSSIvalue and mean distances with outliers removed for Tests A-D with 5 secRSSI reading interval.

The plot of the measured relationship between RSSI value and distancesmay be further refined with mean distances. Here, RSSI values fromtransmitting equipments of the same type and with the same (or similarlyclose) distances to the receiving equipment were combined (averaged).Further, RSSI values from all transmitting equipments with the same (orsimilarly close) distances to the receiving equipment were separatelycombined (averaged). It is noted that a fit to this plot would have lesserror than the previous plots (because of less data points acrosssimilar distances).

FIG. 15E illustrates a graph showing measured relationship between RSSIvalue and total mean distances with outliers removed and an exponentialfit of the graph for Tests A-D with 5 sec RSSI reading interval.

Here, the plot shows the average RSSI values from all transmittingequipments with the same (or similarly close) distances to the receivingequipment. An exponential fit was made to the plot.

FIG. 16 illustrates an exemplary comparison between positions of nodesto SOM evaluated positions (without orientation) for Test B with 5 secRSSI reading interval.

SOM evaluations were performed for the mobile phone arrangements of TestB (using the normalized measured distances as discussed with respect toFIGS. 15A-15E). The resulting SOM evaluations results were compared withthe known positions of the mobile devices arrangement of Test A.

FIG. 17A illustrates a comparison between positions of nodes to SOMevaluated positions after orientation for Test A with 5 sec RSSI readinginterval.

SOM evaluations were performed for the mobile phone arrangements of TestA (using the normalized measured distances as discussed with respect toFIGS. 15A-15E). The SOM evaluations were then oriented using singularvalue decomposition. The resulting oriented SOM evaluations results werecompared with the known positions of the mobile devices arrangement ofTest A.

Referring to FIG. 17A, the SOM evaluation illustrated in FIG. 17A wasperformed with the following parameters: learning rate −0.16 and sigma−3.0 for 300 iterations. The SOM error was 8235.

FIG. 17B illustrates a comparison between positions of nodes to SOMevaluated positions after orientation for Test B with 5 sec RSSI readinginterval.

SOM evaluations were performed for the mobile phone arrangements of TestB (using the normalized measured distances as discussed with respect toFIGS. 15A-15E). The SOM evaluations were then oriented using singularvalue decomposition. The resulting oriented SOM evaluations results werecompared with the known positions of the mobile devices arrangement ofTest B.

Referring to FIG. 17B, the SOM evaluation illustrated in FIG. 17B wasperformed with the following parameters: learning rate −0.16 and sigma−3.0 for 300 iterations. The SOM error was 6622.

FIG. 17C illustrates a comparison between positions of nodes to SOMevaluated positions after orientation for Test C with 5 sec RSSI readinginterval.

SOM evaluations were performed for the mobile phone arrangements of TestC (using the normalized measured distances as discussed with respect toFIGS. 15A-15E). The SOM evaluations were then oriented using singularvalue decomposition. The resulting oriented SOM evaluations results werecompared with the known positions of the mobile devices arrangement ofTest C.

Referring to FIG. 17C, the SOM evaluation illustrated in FIG. 17C wasperformed with the following parameters: learning rate −0.16 and sigma−3.0 for 300 iterations. The SOM error was 7347.

FIG. 17D illustrates a comparison between positions of nodes to SOMevaluated positions after orientation for Test D with 5 sec RSSI readinginterval.

SOM evaluations were performed for the mobile phone arrangements of TestD (using the normalized measured distances as discussed with respect toFIGS. 15A-15E). The SOM evaluations were then oriented using singularvalue decomposition. The resulting oriented SOM evaluations results werecompared with the known positions of the mobile devices arrangement ofTest D.

Referring to FIG. 17D, the SOM evaluation illustrated in FIG. 17D wasperformed with the following parameters: learning rate −0.16 and sigma−3.0 for 300 iterations. The SOM error was 10042.

FIG. 18A illustrates a comparison between positions of nodes to SOMevaluated positions after orientation for Test A with 30 sec RSSIreading interval.

SOM evaluations were performed for the mobile phone arrangements of TestA (using the normalized measured distances as discussed with respect toFIGS. 15A-15E). The SOM evaluations were then oriented using singularvalue decomposition. The resulting oriented SOM evaluations results werecompared with the known positions of the mobile devices arrangement ofTest A.

Referring to FIG. 18A, the SOM evaluation illustrated in FIG. 18A wasperformed with the following parameters: learning rate −0.16 and sigma−3.0 for 600 iterations. The SOM error was 6548.

FIG. 18B illustrates a comparison between positions of nodes to SOMevaluated positions after orientation for Test B with 30 sec RSSIreading interval.

SOM evaluations were performed for the mobile phone arrangements of TestB (using the normalized measured distances as discussed with respect toFIGS. 15A-15E). The SOM evaluations were then oriented using singularvalue decomposition. The resulting oriented SOM evaluations results werecompared with the known positions of the mobile devices arrangement ofTest B.

Referring to FIG. 18B, the SOM evaluation illustrated in FIG. 18B wasperformed with the following parameters: learning rate −0.16 and sigma−3.0 for 600 iterations. The SOM error was 4206.

FIG. 18C illustrates a comparison between positions of nodes to SOMevaluated positions after orientation for Test C with 30 sec RSSIreading interval.

SOM evaluations were performed for the mobile phone arrangements of TestC (using the normalized measured distances as discussed with respect toFIGS. 15A-15E). The SOM evaluations were then oriented using singularvalue decomposition. The resulting oriented SOM evaluations results werecompared with the known positions of the mobile devices arrangement ofTest C.

Referring to FIG. 18C, the SOM evaluation illustrated in FIG. 18C wasperformed with the following parameters: learning rate −0.16 and sigma−3.0 for 600 iterations. The SOM error was 5418.

FIG. 18D illustrates a comparison between positions of nodes to SOMevaluated positions after orientation for Test D with 30 sec RSSIreading interval.

SOM evaluations were performed for the mobile phone arrangements of TestD (using the normalized measured distances as discussed with respect toFIGS. 15A-15E). The SOM evaluations were then oriented using singularvalue decomposition. The resulting oriented SOM evaluations results werecompared with the known positions of the mobile devices arrangement ofTest D.

Referring to FIG. 18D, the SOM evaluation illustrated in FIG. 18D wasperformed with the following parameters: learning rate −0.16 and sigma−3.0 for 600 iterations. The SOM error was 12895.

FIG. 19A illustrates a comparison between positions of nodes to SOMevaluated positions after orientation for Test A with 1 min RSSI readinginterval.

SOM evaluations were performed for the mobile phone arrangements of TestA (using the normalized measured distances as discussed with respect toFIGS. 15A-15E). The SOM evaluations were then oriented using singularvalue decomposition. The resulting oriented SOM evaluations results werecompared with the known positions of the mobile devices arrangement ofTest A.

Referring to FIG. 19A, the SOM evaluation illustrated in FIG. 19A wasperformed with the following parameters: learning rate −0.11 and sigma−9.0 for 800 iterations. The SOM error was 5063.

FIG. 19B illustrates a comparison between positions of nodes to SOMevaluated positions after orientation for Test B with 1 min RSSI readinginterval.

SOM evaluations were performed for the mobile phone arrangements of TestB (using the normalized measured distances as discussed with respect toFIGS. 15A-15E). The SOM evaluations were then oriented using singularvalue decomposition. The resulting oriented SOM evaluations results werecompared with the known positions of the mobile devices arrangement ofTest B.

Referring to FIG. 19B, the SOM evaluation illustrated in FIG. 19B wasperformed with the following parameters: learning rate −0.11 and sigma−9.0 for 800 iterations. The SOM error was 4385.

FIG. 19C illustrates a comparison between positions of nodes to SOMevaluated positions after orientation for Test C with 1 min RSSI readinginterval.

SOM evaluations were performed for the mobile phone arrangements of TestC (using the normalized measured distances as discussed with respect toFIGS. 15A-15E). The SOM evaluations were then oriented using singularvalue decomposition. The resulting oriented SOM evaluations results werecompared with the known positions of the mobile devices arrangement ofTest C.

Referring to FIG. 19C, the SOM evaluation illustrated in FIG. 19C wasperformed with the following parameters: learning rate −0.11 and sigma−9.0 for 800 iterations. The SOM error was 6095.

FIG. 19D illustrates a comparison between positions of nodes to SOMevaluated positions after orientation for Test D with 1 min RSSI readinginterval.

SOM evaluations were performed for the mobile phone arrangements of TestD (using the normalized measured distances as discussed with respect toFIGS. 15A-15E). The SOM evaluations were then oriented using singularvalue decomposition. The resulting oriented SOM evaluations results werecompared with the known positions of the mobile devices arrangement ofTest D.

Referring to FIG. 19D, the SOM evaluation illustrated in FIG. 19D wasperformed with the following parameters: learning rate −0.11 and sigma−9.0 for 800 iterations. The SOM error was 5807.

FIG. 20A illustrates a comparison between positions of nodes to SOMevaluated positions after orientation for Test A with 2 min RSSI readinginterval.

SOM evaluations were performed for the mobile phone arrangements of TestA (using the normalized measured distances as discussed with respect toFIGS. 15A-15E). The SOM evaluations were then oriented using singularvalue decomposition. The resulting oriented SOM evaluations results werecompared with the known positions of the mobile devices arrangement ofTest A.

Referring to FIG. 20A, the SOM evaluation illustrated in FIG. 20A wasperformed with the following parameters: learning rate −0.31 and sigma−13.0 for 800 iterations. The SOM error was 4706.

FIG. 20B illustrates a comparison between positions of nodes to SOMevaluated positions after orientation for Test B with 2 min RSSI readinginterval.

SOM evaluations were performed for the mobile phone arrangements of TestB (using the normalized measured distances as discussed with respect toFIGS. 15A-15E). The SOM evaluations were then oriented using singularvalue decomposition. The resulting oriented SOM evaluations results werecompared with the known positions of the mobile devices arrangement ofTest B.

Referring to FIG. 20B, the SOM evaluation illustrated in FIG. 20B wasperformed with the following parameters: learning rate −0.31 and sigma−13.0 for 800 iterations. The SOM error was 4196.

FIG. 20C illustrates a comparison between positions of nodes to SOMevaluated positions after orientation for Test C with 2 min RSSI readinginterval.

SOM evaluations were performed for the mobile phone arrangements of TestC (using the normalized measured distances as discussed with respect toFIGS. 15A-15E). The SOM evaluations were then oriented using singularvalue decomposition. The resulting oriented SOM evaluations results werecompared with the known positions of the mobile devices arrangement ofTest C.

Referring to FIG. 20C, the SOM evaluation illustrated in FIG. 18C wasperformed with the following parameters: learning rate −0.31 and sigma−13.0 for 800 iterations. The SOM error was 4159.

FIG. 20D illustrates a comparison between positions of nodes to SOMevaluated positions after orientation for Test D with 2 min RSSI readinginterval.

SOM evaluations were performed for the mobile phone arrangements of TestD (using the normalized measured distances as discussed with respect toFIGS. 15A-15E). The SOM evaluations were then oriented using singularvalue decomposition. The resulting oriented SOM evaluations results werecompared with the known positions of the mobile devices arrangement ofTest D.

Referring to FIG. 20D, the SOM evaluation illustrated in FIG. 20D wasperformed with the following parameters: learning rate −0.31 and sigma−13.0 for 800 iterations. The SOM error was 4171.

FIG. 21A illustrates a comparison among the average error rates of SOMevaluated positions with different SOM parameters for 5 sec RSSI readinginterval.

The SOM evaluations for Test A, B, C, and D (some samples of the SOMevaluations were discussed with respect to FIGS. 17A-D, 18A-D, 19A-D,and 20A-D) were performed using a range of parameters. The learningrates used were 0.01, 0.06, 0.11, 0.16, 0.21, 0.26, 0.31, 0.36, 0.41,0.46, and 0.51. The sigma used were from 1.0 to 20.0.

Referring to FIG. 21A, the average SOM error ranged from 8965 to 15286.The SOM parameters leading to the minimum SOM error were 0.16 for thelearning rate and 3.0 for sigma, for 300 iterations of the SOM.

FIG. 21B illustrates a comparison among the average error rates of SOMevaluated positions with different SOM parameters for 30 sec RSSIreading interval.

The SOM evaluations for Test A, B, C, and D (some samples of the SOMevaluations were discussed with respect to FIGS. 17A-D, 18A-D, 19A-D,and 20A-D) were performed using a range of parameters. The learningrates used were 0.01, 0.06, 0.11, 0.16, 0.21, 0.26, 0.31, 0.36, 0.41,0.46, and 0.51. The sigma used were from 1.0 to 20.0.

Referring to FIG. 21B, the average SOM error ranged from 6555 to 14687.The SOM parameters leading to the minimum SOM error were 0.16 for thelearning rate and 3.0 for sigma, for 600 iterations of the SOM.

FIG. 21C illustrates a comparison among the average error rates of SOMevaluated positions with different SOM parameters for 1 min RSSI readinginterval.

The SOM evaluations for Test A, B, C, and D (some samples of the SOMevaluations were discussed with respect to FIGS. 17A-D, 18A-D, 19A-D,and 20A-D) were performed using a range of parameters. The learningrates used were 0.01, 0.06, 0.11, 0.16, 0.21, 0.26, 0.31, 0.36, 0.41,0.46, and 0.51. The sigma used were from 1.0 to 20.0.

Referring to FIG. 21C, the average SOM error ranged from 5662 to 14907.The SOM parameters leading to the minimum SOM error were 0.11 for thelearning rate and 9.0 for sigma, for 800 iterations of the SOM.

FIG. 21D illustrates a comparison among the average error rates of SOMevaluated positions with different SOM parameters for 2 min RSSI readinginterval.

The SOM evaluations for Test A, B, C, and D (some samples of the SOMevaluations were discussed with respect to FIGS. FIGS. 17A-D, 18A-D,19A-D, and 20A-D) were performed using a range of parameters. Thelearning rates used were 0.01, 0.06, 0.11, 0.16, 0.21, 0.26, 0.31, 0.36,0.41, 0.46, and 0.51. The sigma used were from 1.0 to 20.0.

Referring to FIG. 21D, the average SOM error ranged from 5061 to 14827.The SOM parameters leading to the minimum SOM error were 0.31 for thelearning rate and 13.0 for sigma, for 800 iterations of the SOM.

To avoid unnecessarily obscuring the present disclosure, the precedingdescription may omit a number of known structures and devices. Thisomission is not to be construed as a limitation of the scopes of theclaims. Specific details are set forth to provide an understanding ofthe present disclosure. It should however be appreciated that thepresent disclosure may be practiced in a variety of ways beyond thespecific detail set forth herein.

Furthermore, while the exemplary aspects, embodiments, and/orconfigurations illustrated herein show the various components of thesystem collocated, certain components of the system can be locatedremotely, at distant portions of a distributed network, such as a LANand/or the Internet, or within a dedicated system. Thus, it should beappreciated, that the components of the system can be combined into oneor more devices, or collocated on a particular node of a distributednetwork, such as an analog and/or digital telecommunications network, apacket-switch network, or a circuit-switched network. It will beappreciated from the preceding description, and for reasons ofcomputational efficiency, that the components of the system can bearranged at any location within a distributed network of componentswithout affecting the operation of the system. For example, the variouscomponents can be located in a switch such as a PBX and media server,gateway, in one or more communications devices, at one or more users'premises, or some combination thereof. Similarly, one or more functionalportions of the system could be distributed between a telecommunicationsdevice(s) and an associated computing device.

Furthermore, it should be appreciated that the various links connectingthe elements can be wired or wireless links, or any combination thereof,or any other known or later developed element(s) that is capable ofsupplying and/or communicating data to and from the connected elements.These wired or wireless links can also be secure links and may becapable of communicating encrypted information. Transmission media usedas links, for example, can be any suitable carrier for electricalsignals, including coaxial cables, copper wire and fiber optics, and maytake the form of acoustic or light waves, such as those generated duringradio-wave and infra-red data communications.

Also, while the flowcharts have been discussed and illustrated inrelation to a particular sequence of events, it should be appreciatedthat changes, additions, and omissions to this sequence can occurwithout materially affecting the operation of the disclosed embodiments,configuration, and aspects.

A number of variations and modifications of the disclosure can be used.It would be possible to provide for some features of the disclosurewithout providing others.

In yet another embodiment, the systems and methods of this disclosurecan be implemented in conjunction with a special purpose computer, aprogrammed microprocessor or microcontroller and peripheral integratedcircuit element(s), an ASIC or other integrated circuit, a digitalsignal processor, a hard-wired electronic or logic circuit such as adiscrete element circuit, a programmable logic device or gate array suchas PLD, PLA, FPGA, PAL, special purpose computer, any comparable means,or the like. In general, any device(s) or means capable of implementingthe methodology illustrated herein can be used to implement the variousaspects of this disclosure. Exemplary hardware that can be used for thedisclosed embodiments, configurations and aspects includes computers,handheld devices, telephones (e.g., cellular, Internet enabled, digital,analog, hybrids, and others), and other hardware known in the art. Someof these devices include processors (e.g., a single or multiplemicroprocessors), memory, nonvolatile storage, input devices, and outputdevices. Furthermore, alternative software implementations including,but not limited to, distributed processing or component/objectdistributed processing, parallel processing, or virtual machineprocessing can also be constructed to implement the methods describedherein.

In yet another embodiment, the disclosed methods may be readilyimplemented in conjunction with software using object or object-orientedsoftware development environments that provide portable source code thatcan be used on a variety of computer or workstation platforms.Alternatively, the disclosed system may be implemented partially orfully in hardware using standard logic circuits or VLSI design. Whethersoftware or hardware is used to implement the systems in accordance withthis disclosure is dependent on the speed and/or efficiency requirementsof the system, the particular function, and the particular software orhardware systems or microprocessor or microcomputer systems beingutilized.

In yet another embodiment, the disclosed methods may be partiallyimplemented in software that can be stored on a storage medium, executedon programmed general-purpose computer with the cooperation of acontroller and memory, a special purpose computer, a microprocessor, orthe like. In these instances, the systems and methods of this disclosurecan be implemented as a program embedded on a personal computer such asan applet, JAVA® or CGI script, as a resource residing on a server orcomputer workstation, as a routine embedded in a dedicated measurementsystem, system component, or the like. The system can also beimplemented by physically incorporating the system and/or method into asoftware and/or hardware system.

Although the present disclosure describes components and functionsimplemented in the aspects, embodiments, and/or configurations withreference to particular standards and protocols, the aspects,embodiments, and/or configurations are not limited to such standards andprotocols. Other similar standards and protocols not mentioned hereinare in existence and are considered to be included in the presentdisclosure. Moreover, the standards and protocols mentioned herein andother similar standards and protocols not mentioned herein areperiodically superseded by faster or more effective equivalents havingessentially the same functions. Such replacement standards and protocolshaving the same functions are considered equivalents included in thepresent disclosure.

The present disclosure, in various aspects, embodiments, and/orconfigurations, includes components, methods, processes, systems and/orapparatus substantially as depicted and described herein, includingvarious aspects, embodiments, configurations embodiments, subcombinations, and/or subsets thereof. Those of skill in the art willunderstand how to make and use the disclosed aspects, embodiments,and/or configurations after understanding the present disclosure. Thepresent disclosure, in various aspects, embodiments, and/orconfigurations, includes providing devices and processes in the absenceof items not depicted and/or described herein or in various aspects,embodiments, and/or configurations hereof, including in the absence ofsuch items as may have been used in previous devices or processes, e.g.,for improving performance, achieving ease and/or reducing cost ofimplementation.

The foregoing discussion has been presented for purposes of illustrationand description. The foregoing is not intended to limit the disclosureto the form or forms disclosed herein. In the foregoing description forexample, various features of the disclosure are grouped together in oneor more aspects, embodiments, and/or configurations for the purpose ofstreamlining the disclosure. The features of the aspects, embodiments,and/or configurations of the disclosure may be combined in alternateaspects, embodiments, and/or configurations other than those discussedabove. This method of disclosure is not to be interpreted as reflectingan intention that the claims require more features than are expresslyrecited in each claim. Rather, as the following claims reflect,inventive aspects lie in less than all features of a single foregoingdisclosed aspect, embodiment, and/or configuration. Thus, the followingclaims are hereby incorporated into this description, with each claimstanding on its own as a separate preferred embodiment of thedisclosure.

Moreover, though the description has included a description of one ormore aspects, embodiments, and/or configurations and certain variationsand modifications, other variations, combinations, and modifications arewithin the scope of the disclosure, e.g., as may be within the skill andknowledge of those in the art, after understanding the presentdisclosure. It is intended to obtain rights which include alternativeaspects, embodiments, and/or configurations to the extent permitted,including alternate, interchangeable and/or equivalent structures,functions, ranges or steps to those claimed, whether or not suchalternate, interchangeable and/or equivalent structures, functions,ranges or steps are disclosed herein, and without intending to publiclydedicate any patentable subject matter.

1. A method for wireless location, comprising: collecting a plurality ofsignal strength values from a plurality of nodes over a time interval,wherein the signal strength values are representative of signalstrengths of respective plurality of wireless signals transmitted by atleast one of the nodes to at least one other of the nodes, and whereinthe nodes are located in an area; normalizing the collected signalstrength values; and evaluating respective locations within the area ofthe nodes based on the normalized signal strength values.
 2. The methodof claim 1, further comprising orienting the location coordinates to anorientation representative of an orientation of the nodes.
 3. The methodof claim 1, wherein the area includes an indoor facility. 4-5.(canceled)
 6. The method of claim 1, wherein a distance between at leasttwo of the nodes is less than 5 m.
 7. The method of claim 1, wherein thenodes comprise mobile devices.
 8. The method of claim 1, wherein atleast one of the nodes include a different equipment for transmitting orreceiving the wireless signal than at least one other of the nodes.9-10. (canceled)
 11. The method of claim 1, wherein the wireless signalscomprise Bluetooth signals.
 12. The method of claim 1, wherein thewireless signals comprise wireless signals of communication in a meshnetwork, and wherein at least two of the nodes are part of the meshnetwork.
 13. The method of claim 1, wherein the normalizing comprisesnormalizing the signal strength values with a normalization function,wherein the normalization function is based on a fit of previouslycollected signal strength values to a pre-determined normalizationrange.
 14. The method of claim 13, wherein the previously collectedsignal strength values are collected from nodes with similar groups ofwireless equipments as the nodes for transmitting or receiving thewireless signals.
 15. The method of claim 13, wherein the fit comprisesan exponential fit.
 16. The method of claim 13, wherein thenormalization function is based on a fit of averages of the previouslycollected signal strength values of nodes at a substantially samedistance.
 17. (canceled)
 18. The method of claim 1, wherein theevaluating comprises evaluating using machine learning technique on thenormalized signal strength values.
 19. The method of claim 18, whereinthe machine learning technique comprises a self-organizing map (SOM).20-21. (canceled)
 22. The method of claim 18, wherein the machinelearning technique is trained to minimize an error to the locations.23-26. (canceled)
 27. The method of claim 1, further comprisingtransmitting respective data to at least one of the nodes forcontrolling respective displays of the nodes based on the data.
 28. Themethod of claim 27, wherein the transmitting is synchronized with musicplaying in the area in substantially real-time.
 29. (canceled)
 30. Themethod of claim 12, wherein the evaluating is performed by the meshnetwork.
 31. A wireless location system, comprising: a plurality ofwireless nodes positioned in an area, wherein at least one of the nodesis transmitting one or more wireless signals in the area, wherein thewireless signals are received by at least one of the other nodes,wherein signal strengths of the respective wireless signals are detectedby the at least one other nodes as respective signal strength values;and one or more computational equipments configured for normalizing thecollected signal strength values and evaluating respective locationswithin the area of the nodes based on the normalized signal strengthvalues. 32-60. (canceled)
 61. A method for wireless location,comprising: collecting a plurality of signal strength values from aplurality of nodes over a time interval, wherein the signal strengthvalues are representative of signal strengths of respective plurality ofwireless signals transmitted by at least one of the nodes to at leastone other of the nodes, and wherein the nodes are located in an area;normalizing the collected signal strength values, wherein thenormalizing comprises normalizing the signal strength values with anormalization function, wherein the normalization function is based on afit of previously collected signal strength values to a pre-determinednormalization range; evaluating respective locations within the area ofthe nodes based on the normalized signal strength values, wherein theevaluating comprises evaluating using machine learning technique on thenormalized signal strength values, and wherein the machine learningtechnique comprises a self-organizing map (SOM); and orienting thelocation coordinates to an orientation representative of an orientationof the nodes, wherein the area includes an indoor facility less than 40m by 40 m, wherein a distance between at least two of the nodes is lessthan 5 m, wherein the nodes comprise mobile devices. 62-81. (canceled)